Christine Joblin

Stephane Thomas

Sabine Kimmel

Frederic Arenou

David Katz

Noël Robichon

Monique Et Franc ¸ois Spite

Catherine Turon

Fréderic Royer

Clément Hottier

Carine Babusiaux

Laura Ruiz Dern

Elis- Abetha Caffau

Jonathan Smoker

Amin Farhang

Peter J Sarre

Farid Salama

Evelyne Roueff

Xavier Bacalla

Harold Linnartz

Taha Shisseh

Hamza Ajeddig

Abdelkarim Boskri

Khalid Berkaoui

Meryem Guennoun

Youssef El Jariri

Jamila Rguig

Amal Loutfi

Youssef Moulane

Dalal El

Sébastien Fontaine

Alain Redding

Thebault Philippe

Monica Rotaru

Nathalie Berthier

Dominique Richard

Franc ¸ois Sylvain Colas

Brigitte Bouley

Zanda

Merci D'

Tabatha Sauvaget

Diane Berard

Charlotte Gehan

Isadora Bicalho

Sans Oublier

Guillaume Voisin

Alexandre Le Tiec

Friedriech Anders

Anelise Audibert

Eduardo Balbinot

Benoit Tabone

William Polycarpe

Prasanna Deshapriya

Jean-Baptiste Vielfaure

Miguel Marques

L Capitanio
email: letizia.capitanio@obspm.fr

Rosine Lallement

J L Vergely

Meriem Elyajouri

Ana Monreal-Ibero

Nick L J Cox

Jan Cami

Charlotte C M Marshall

Keith T Smith

Christopher J Evans

Pierre Royer

Martin A Cordiner

Jacco Th Van Loon

Bernard H Foing

Neil H Bhatt

Emeric Bron

Alex De Koter

Pascale Ehrenfreund

Atefeh Javadi

Lex Kaper

Habib G Khosroshadi

Mike Laverick

Franck Le Petit

Giacomo Mulas

Marco Spaans

A mes amis de l'Uranoscope, en particulier Christian Bourdeille et

Keywords: dust, extinction -ISM, lines and bands -ISM, structure -ISM, supernova remnants -solar neighborhoodlocal insterstellar matter

We shall not cease from exploration, and the end of all our exploring will be to arrive where we started and know the place for the first time.'

T. S. Eliot

Je tiens à exprimer ma reconnaissance à toutes celles et tous ceux qui m'ont encouragée et qui ont contribué à la réalisation de la présente thèse. Au-delà du grade universitaire, une thèse de doctorat est avant tout une aventure humaine. Et je n'ai pas vu passer les trois dernières années, tellement elles furent riches en émotion, car marquées par des rencontres fascinantes, des données captivantes, des collaborations fructueuses, et surtout beaucoup d'épanouissement. Tout d'abord je remercie le Domaine d'Intérêt Majeur en Astrophysique et Conditions d'Apparition de la Vie (DIM ACAV) de la Région Île-de-France pour avoir apporté le financement nécessaire à ce projet.

Je tiens à remercier, avec toute mon amitié, ma directrice de thèse Rosine Lallement, à qui je dois ce sujet de recherche. Cette thèse est le fruit d'une collaboration de plus de trois années au sein de son équipe. Une équipe que j'ai intégrée un peu malgré moi car au tout début j'étais décidée à ne faire qu'un stage, mais j'y suis finalement restée plus de trois ans et c'est en grande partie grâce à Rosine. Son encadrement de qualité, son soutien moral et son ouverture d'esprit m'ont donné le courage d'aller jusqu'au bout. Je tiens enfin à la remercier de m'avoir fait assez confiance pour me laisser réaliser un projet qui me tenait à coeur, le SpaceBus. Je lui en suis toujours reconnaissante.

Mes remerciements s'adressent également à Ana Monreal-Ibero qui m'a coencadrée pendant mon stage du master et durant la première année de cette thèse. Merci d'avoir quotidiennement trouvé du temps entre deux codes Python pour m'accueillir dans la bonne humeur et discuter de l'avancement de mes travaux.

Je tiens à remercier très chaleureusement le jury, qui a porté une grande attention à mon travail et m'a donné de précieux conseils. Je remercie en particulier mes deux rapporteurs, Paule Sonnentrucker d'être venue des États-Unis et Franc ¸ois-Xavier Désert de Grenoble et de leur disponibilité bien qu'ils soient très sollicités.

A mes amis de la cité universitaire internationale de Paris et tout particulièrement ceux avec qui j'ai cohabité à la maison du Maroc et qui m'ont très souvent encouragée ou accompagnée pendant les nuits blanches à la bibliothèque : Mostafa Mouaddine (surtout pour les fajitas), Mehdi Amraoui (pour les balades nocturnes), Youssef Mourchid (pour les cafés vanille), et la trésorerie team, Zakaria Sebaa, Samih Smidi, Abderafie, Mohamed Ahib, pour leur bonne humeur et leur encouragement.

Merci à la famille Irzi, de me sentir chez eux comme dans une seconde famille. A Soukaina et Fati, merci pour les escapades en voiture, les confidences le long de la Seine et les repas bien caloriques du dimanche et des fêtes.

Merci à la UIR squad Paris, Hind El Messaoudi et Mehdi El Hakimi d'avoir été toujours partants pour partager une danse, Aymane Rbai pour les oneman-shows, les concerts de Jazz et les tours en trottinette, Marwane Boutfirass pour David August, les mix et les soirées loup-garou et Issam Benabdelkader pour les débats passionnants. Je suis heureuse de pouvoir toujours compter sur vous depuis la prépa.

A mes amis d'enfance, Anass Cherkaoui Dekkaki que le hasard de la vie a remis sur mon chemin, Aqil Cheddadi et Chahrazad Bekkaoui. Grâce à vous le papillon n'oublie pas qu'il a été chenille.

A Sofia Drissi, que je n'ai s ûrement jamais assez remerciée d'avoir été là tout au long de cette aventure, et surtout d'avoir supporté mes insomnies et mon humeur matinale. Merci d'être devenue une véritable confidente. Avec ix toute mon affection, j'ai également une pensée pour toute la famille Drissi pour son soutien constant.

A Gatsby le magnifique, merci d'avoir su trouver les mots, en particulier les fameux kika, pour me faire rire et m'apaiser lorsque je doutais. Merci pour l'intrication qui m'a permis de survivre tout au long de ma thèse. Merci pour nos soirées passées à refaire le monde autour d'un verre, au coin d'une rue ou sur un air de Tchaïkovsky. Merci d'avoir cru en moi comme j'aimerai croire en moi-même.

Enfin, je suis infiniment reconnaissante à ma famille, et surtout à mes parents. Merci Papa, merci Maman pour m'avoir soutenue dans mes choix, quels qu'ils soient. A Omar d'avoir cru en cette petite soeur têtue et pour son soutien sans faille, merci d'avoir toujours ajouté une touche d'originalité à mes projets. Sache que je crois beaucoup en toi. A Hakim pour ses défis et surtout pour sa méthode de travail qui a beaucoup influencé la mienne, sans oublier son go ût pour le café et la musique. A Imane ma Nounna, je la remercie tout particulièrement de son aide pendant la phase terminale de cette thèse, et du coup de main dans l'organisation du workshop et du fameux pot de thèse. J'ai une pensée particulière également à mes grandsparents, Tata Leila, Oumama et surtout au petit dernier, Ismail, qui sans le savoir, m'a donné des moments d'insouciance dans ces derniers mois de rédaction. Vous êtes le moteur de ma progression.

x

Abstract

The interstellar species responsible for the Diffuse Interstellar Bands (DIBs), the hundreds of irregular absorptions observed since decades in spectra of reddened stars or of galaxies, are still unknown, at the exception of several bands now very convincingly attributed to the buckminsterfullerene cation C 60 + . There is a large consensus recently reached about the nature of the DIB carriers, thought to be large carbonaceous molecules in the gaseous phase of the interstellar medium (ISM). These complex molecules are one of the links in the process cycle linked to the formation of stars (and their planets) by collapse of clouds until the end of the stars life, their explosion and ejection of modified materials into space, matter that will feed subsequent generations of stars. This evolutionary cycle is fundamental and one of the pillars of astrophysics, many outstanding questions are associated with it (evolution of galaxies, dark matter, etc.). Being likely a very large, if not the largest reservoir of organic matter in the Universe, the search for DIB carriers properties and identifications is more than ever of fundamental interest.

The identification of a DIB carrier requires irrefutable coincidences between the observed spectral signatures of a given species in laboratory spectra and their counterparts in astronomical spectra. This proved to be extremely difficult and only the fullerene cation passed this test, as indicated above. One of the difficulties lies in the large and everincreasing number of mostly uncorrelated DIBs and in the consensus idea that this variety implies the existence of an equally large number of different macromolecules. The laboratory match with astronomical observations can be optimized if the sheer number of possible species to be studied in the laboratory can be limited by constraints on the relevant and realistic spatial conditions, i.e., molecules and ions that should be free, cold (<100 K) and exposed to some sort of energy mostly VUV (vacuum UV) photons. Such preselection can be carried out through in-depth studies of the physical and chemical properties of the DIB carriers. These properties can be based on the analysis of very detailed multi-species data from high spectral resolution observations, statistical studies based on large spectroscopic surveys or specific tomographic studies of selected clouds. The work presented in this thesis corresponds to a combination of these three different observational aspects.

All types of DIB spectral analyses have common data cleaning requirements, in particular the removal of telluric lines, the untangling of stellar-interstellar features and DIB extraction using specific codes and stacking methods, and finally the search for information on dust, known gaseous species and the physical properties associated with each line of sight.

To do this, during this thesis, I learned to deal with these different aspects, to improve or design adapted spectral analysis codes and I was able to contribute to several innovative techniques in related DIB issues. xi I have also learned to use extended databases from large programs dedicated to infrared or visible spectroscopy of the stars of the Milky Way.

The work presented in this document is based on spectroscopic surveys and specific observation programs. I made extensive use of the high quality spectra of the SDSS/APOGEE near infrared (NIR) survey, I performed the first analyses of the high resolution-high signal optical spectra of the more recent EDIBLES survey at the VLT, I used the NIR data of a specific program recorded with KMOS at the VLT and, finally, TBL/NARVAL and CFHT/EspaDons optical spectra from the archive or specific programs. The main products of the thesis work are of several types:

• new extended catalogs of NIR DIBs,

• identification of potential new DIBs,

• the first 3D tomography of a DIB in a single dense cloud,

• additional links between ionizing conditions, opacity of dust, molecular content and DIB strength,

• new spectral profiles of about 30 DIBs with many additional details, including the particularly interesting series of low DIBs associated with C 2 molecules and opaque clouds,

• a first empirical relationship between DIB strength, dust extinction and Planck optical depth of emitting dust for off-Plane nearby clouds.

All these results provide additional clues in the search for the formation and destruction of DIB carriers:

• catalogs are ingredients for 3D mapping of a DIB carrier or more generally additional ingredients for 3D mapping of interstellar clouds,

• new DIB candidates and substructures in DIB profiles constrain future identification in the laboratory,

• DIB tomography in individual clouds, hierarchy of DIBs in response to the ionization field and molecules and links with dust absorption and emission may help constraining the processes of formation/destruction/ionization of DIB carriers around and within the molecular clouds.

xii Chapter 1

Introduction

-Here truly is a hole in the sky-William Herschel

Our Galaxy

If one ever gets a chance to walk away from the city, far from the light pollution (or moonlight), one might see the spectacular vision of a huge cosmic city traced over the sky (Fig. 11). This is our Galaxy, the Milky Way, who appears as an extended white trail across the night sky. Unfortunately, being inside the Galaxy, our view is limited to this disk-shaped structure in profile.

The Milky Way is believed to be a barred spiral galaxy, and what we see when we look at this hazy light band of stars is our place from within. We are a part of an enormous star gathering, and the Sun is only one of the 100-400 billion stars that composed spiral arms of the Galaxy.

Once we look at the Milky Way from inside the disk, there appears to be more than stars. The naked eye can distinguish large dark regions -clouds where gas and dust are intimately mixed-which rub shoulders with bright areas, referred to as nebulae which are mainly composed of gas illuminated by nearby stars. These dark spots are produced by dust 1 . The clouds pass in front of stars that try to pierce them with light but some clouds manage to completely mask starlight.

Introduction

Figure 11 A view of the Milky Way as seen from a dark site with little light pollution (Oukaimeden Observatory, Morocco). Courtesy: Observatory of Oukaimeden's team.

In fact, the space between the stars is not empty. It contains large quantities of gas and dust called the interstellar medium (ISM). Clouds of all dimensions are pressed in the disk of our galaxy creating some of the most beautiful objects of deep space as illustrated in Fig. 12.

However, the interstellar medium is more than a work of art: it holds the secret of the birth, formation, and death of stars.

1.2 A Little Bit of History

Discovery of dust

In 1784, William Herschel pointed his telescope towards the Scorpius constellation, where he detected an unusual vacant area. This observation lead him to publish "On the Construction of the Heavens" the following year, a paper where he described this detection as "a hole in the sky" [START_REF] Herschel | On the Construction of the Heavens[END_REF]). Herschel's discovery went unnoticed until the correspondence between his sister Caroline and his son John Herschel in 1833 where she asked him to "see if there is not something remarkable in the lower part of the Scorpion to be found [...] It was something more than a total absence of stars" (the entire correspondence can be found in a recent historical review by Wolfgang Steinicke (Steinicke 2016)). Unfortunately, none of them had understood the hole's cause.

A better understanding was reached in the first quarter of the twentieth century when Edward Emerson Barnard used astrophotography to map out these dark regions and published the first photographic atlas of 182 "dark markings of the sky" [START_REF] Barnard | On the dark markings of the sky, with a catalogue of 182 such objects[END_REF]) 2 . Figure 13 shows an example of these regions near Theta Ophiuchi. Barnard regarded many of these dark patches as "really obscuring bodies nearer to us than the distant stars". Soon after came the works of Bart J. Bok, who was the first to point a link between Barnard objects and stellar formation through analyzing the inner compact cores, which would later be named after him [START_REF] Bok | Dimensions and Masses of Dark Nebulae // Harvard Observatory Monographs[END_REF].

Barnard clouds and Bok globules are now known as dark clouds (or dense clouds). They appear dark because they do not emit at optical wavelengths and their dust blocks the light of the stars. This extinction property (the dimming of the light from the stars) was introduced by Robert Jules [START_REF] Trumpler | Absorption of Light in the Galactic System[END_REF], who viewed the interstellar matter as a composition of free atoms, like Ca and Na, as well as "fine cosmic dust particles of various sizes" which produce the excess of colors by Rayleigh scattering.

Introduction

Figure 13 Dark Markings near Theta Ophiuchi. Plate chart number 20 from "A Photographic Atlas of Selected Regions of the Milky Way" by [START_REF] Barnard | A Photographic Atlas of Selected Regions of the Milky Way[END_REF]. The chart shows the region around Theta Ophiuchi star (380). Note the dark S-shaped nebulae B72 (known as the Snake Nebula). Also seen at the north are the curled nebulae B75 and B262. Here, a high concentration of dust and molecular gas absorb practically all visible light emitted from background stars.

Discovery of gas

The first observational evidence of a non-empty environment between the stars comes from spectroscopy. In the winters of 1901/1902and 1902/1903, Johannes Franz Hartmann observed a series of binary stars, including δ Orionis star from the Orion's belt. [START_REF] Hartmann | Investigations on the spectrum and orbit of delta Orionis[END_REF] reported, in the spectrum of this latter, the presence of the stationary CaII 'H' and 'K' absorption lines at 3933 and 3968 Å. One expects a Doppler shift for lines linked to a stellar atmosphere, but those of CaII did not show a sign of wavelength variation. This led Hartmann to conclude that this gas must arise from the intervening medium (the line of sight between the star and Earth) rather than the atmosphere of δ Orionis star. This discovery was the first evidence of the existence of the interstellar matter but was not largely accepted by the scientific community (e.g. [START_REF] Young | The Calcium Lines H and K in Early Type Stars // Publications of the Dominion Astrophysical Observatory Victoria[END_REF]). We had to wait until [START_REF] Plaskett | Determination of the K-term, the solar motion, and the galactic rotation from the radial velocities of 849 O to B7 stars[END_REF] work to firmly establish the interstellar origin of these features.

Since then, various detections and identifications were reported. The first observations of molecules in the ISM go back to 1941. Taking advantage of the high-resolution optical spectroscopy, diatomic molecules such as CH, Table 11 Major discoveries and investigations that had a significant impact on the ISM field in the 20th century [1900's-1970's].

CH + and CN were detected in a diffuse cloud. During the twentieth century, our knowledge of the ISM has significantly evolved thanks in particular to laboratory works and to the advent of radio astronomy era (initiated by the discovery of the HI 21-cm line). Moreover, the increasing number and the improvement of observation techniques (CCD) and ground-based telescopes had strongly impacted this field. Table 11 lists some of the highlights of the twentieth century. Starting in the 1970s, space astronomy has opened new wavelength windows to the ISM research field. This has allowed the observation of many unobserved interstellar species (inaccessible from the ground because of telluric lines) through their absorption spectra in the UV range by the satellite Copernicus. This latter has detected many abundant species, including the major discovery of H 2 by [START_REF] Carruthers | Rocket Observation of Interstellar Molecular Hydrogen[END_REF]. Then followed the International Ultraviolet Explorer (IUE), the Hubble Space Telescope (HST) and the Far Ultraviolet Spectroscopic Explorer (FUSE).

This is raising awareness of the importance of the ISM in astrophysics. In fact, the interstellar medium, initially seen as a homogeneous and uniform medium without much interest, became a dynamic and turbulent medium, and the place of birth and death of the stars. The outer galaxies3 also having their interstellar medium, its understanding is of particular importance in the galaxy evolution context. Finally, the study of the ISM is also primordial being systematically a foreground for any extra-solar observation.

1.3. The Interstellar Medium the galactic disc and halo. The typical densities are low in the range of 10 -3 to 10 -2 cm -3 while the kinetic temperature is ∼ 10 6 K.

• Warm Ionised Medium (WIM) or HII gas: The hydrogen atoms are ionized by the photons (with energy hν > 13.6 eV) emitted by the surrounding stars. The H+ ions formed have the ability to recombine with the free electrons. But the process of photoionization is faster than that of recombination, which leads to a medium mostly ionized with a temperature T of ∼ 10 4 K and a particle number density varying between 0.2 and 0.5 cm -3 .

• Warm Neutral Medium (WNM) or warm HI: In this medium, hydrogen is neutral but has the same temperature as the WIM and a density n ≈ 0.6 cm -3 . Both constitute a large fraction of the volume of the total gas ( 50%) and generally represent the outer envelopes of a cloud.

• Cold Neutral Medium (CNM): This is the coldest (from 10 to 100 K) and densest (20 to 50 cm -3 ) phase of the interstellar medium, it is also the largest in mass but remains the lowest in volume. It consists of mainly hydrogen in atomic form and represents the inside of the diffuse clouds shielded from the ionizing radiation. H is neutral due to shielding against photons but still heated by starlight and cosmic rays.

Both with WNM can be detected thanks to the line 21 cm from the atomic hydrogen. Note that, within the meaning of this classification, the CNM also includes the molecular phase (see below).

The review by [START_REF] Ferrière | The interstellar environment of our galaxy // Reviews of Modern Physics[END_REF] detailed the information on the densities and temperatures.

All CNM, WNM, WIM and HIM phases are found in the diffuse interstellar medium.

In the densest areas of the CNM, we find hydrogen in its molecular form:

• Molecular Medium (Molecular Medium, MM): This phase occupies a very small portion of the interstellar medium but contains 30-60% of its mass thanks to its high density n ≈ 102 -106 cm -3 .

One has to keep in mind that classifying the ISM into phases has its limitations and remains very schematic. Being heterogeneous and turbulent place, the ISM contains overlapping regions where boundaries are not sharp and hard to separate. For e.g., there are regions dominated by photons known as photodissociation or photodominated region (PDR). It is a sort of a transition between the molecular cloud phase and the WNM or WIM regions surrounding the central star.

• PDR regions: Here, there is non-ionizing radiation for atomic hydrogen (hν < 13.6 eV), but the dissociation for molecular hydrogen (hν >

1. Introduction 11.2 eV) generates a layer at the interface between the dense medium where the star was born and the HII region it created.

The atomic and molecular cold neutral medium can be divide into four types as introduced in a recent classification by Snow, McCall (2006a). This classification is based on the state of hydrogen and carbon in the gas. Hereafter, this following classification is the one used to distinguish the different environments studied in this thesis:

• Diffuse atomic clouds are found throughout our Galaxy, but they are mainly located near the galactic plane. They are generally characterized by low density n ≤ 103 cm -3 (or Av ≤ 2), cold (10 -100 K) and are completely exposed to the interstellar radiation field.

• Diffuse molecular clouds When the density of the diffuse atomic clouds becomes sufficient (Av≥0.2 mag and n ≈ 100 -500 cm -3 ) to protect the hydrogen atoms from radiation, H 2 molecules can be formed without being instantly photodissociated. The presence of H 2 triggers complex chemistry as the reactions of the type X + + H 2 --→ XH + + H and then leads to the efficient production of H + 3 which is involved in many of the chemical reactions producing more complex molecules [START_REF] Herbst | The Formation and Depletion of Molecules in Dense Interstellar Clouds[END_REF]. In the other hand, the radiation in these clouds remains sufficient to photoionize the atomic carbon C and to photodissociate CO, the carbon is then mostly in C + form which initiate the organic chemistry [START_REF] Van Dishoeck | Comprehensive models of diffuse interstellar clouds -Physical conditions and molecular abundances[END_REF]. The kinetic temperature is in the range of [30 -100] K.

• Translucent clouds are transitional objects between the diffuse and dense molecular clouds with a kinetic temperature of ≈ 15 to 50 K. They are defined as clumps of interstellar material with visual extinction between 1 and 5 magnitudes and density varying from 100 to 1000 cm -3 . Various translucent clouds have been detected using Spitzer [START_REF] Ingalls | Spitzer Infrared Spectrograph Detection of Molecular Hydrogen Rotational Emission towards Translucent Clouds[END_REF]. Prior to Spitzer, many molecules (H2, CH, CN, CO) have been detected in these clouds, for e.g. mm-wave CO absorption [START_REF] Liszt | The structure and stability of interstellar molecular absorption line profiles at radio frequencies[END_REF] and FUSE [START_REF] Rachford | A Far Ultraviolet Spectroscopic Explorer Survey of Interstellar Molecular Hydrogen in Translucent Clouds[END_REF], Sonnentrucker et al. (2007a)).

• Dark/dense molecular clouds can give birth to dense cores under the effect of a collapse induced by gravity. In these regions, we find the highest densities of the ISM (10 3 -10 5 cm -3 ) and the visual extinction A V exceeds 5 magnitudes. In these dark cores, the warming of the environment is mainly due to the impact of cosmic rays on grains and gas-grain collisions (Burke, Hollenbach 1983). The lines of the CO molecule make it possible to trace these dense clouds, because the H 2 molecule is difficult to observe, its electronic transitions in ultraviolet and ro-vibration transitions in the mid-infrared make it not observable from the ground-based telescopes.

1. Introduction effect of gravitational condensation, the coldest phases form diffuse clouds which contract and collapse again to evolve towards molecular clouds. This exchange of matter between the stars and the interstellar medium is permanent and occurs not only within of any galaxy but also in the intergalactic medium between galaxies.

The Interstellar Matter

By mass, the interstellar matter is composed primarily of 70.4% hydrogen (H) and 28.1% helium (He) with trace amounts of 1.5% of heavier elements. The latter are generally metals 5 such as oxygen (O), carbon (C), nitrogen (N), magnesium (Mg), Silicium (Si), iron (Fe) and sulphur (S). These elements are found in gas phase and in solid dust grains as well.

Interstellar Gas

The interstellar gas absorption lines, in both atomic and molecular form, are very narrow and sharp (in the single-cloud situation), indicating low temperatures and at line shifts indicative of the velocities of interstellar gas clouds6 .

• Tracers of atomic gas

In emission: the neutral atomic gas is commonly traced via the 21 cm hyperfine structure line of hydrogen. In the far infrared, lines of carbon and oxygen atoms and ions are also observed. Transitions lines of heavier species such as argon (Ar), neon (Ne) or sulphur (S) are associated with the most irradiated and ionized regions.

In absorption: Most of the interstellar species have their transitions in the UV range such as Ly α at 1216 Å but the telluric absorption make it difficult to observe from ground-based telescopes. Therefore, the most common absorption tracers are the neutral sodium (NaI) lines, namely the doublet D2 and D1, at 5889.95 and 5895.92 Å, and ionized calcium (CaII or Ca + ), namely K and H lines, at 3933.66 and 3968.47 Å. NaI is neutral gas tracer more associated with denser cloud, whereas CaII is neutral and partially ionized gas tracer more associated with diffuse clouds. There are also other gaseous tracers such as potassium (KI) at 7665 and 7699 Å) indicative of dense clouds or the "blue" CaI at 4227 Å).

The Interstellar Matter

• Tracers of molecular gas: The molecular gas is found primarily in dense regions (dark clouds, dense cores and protoplanetary disks) where it is shielded against the dissociating effects of UV photons.

Despite being the most abundant molecule, the presence of H 2 can only be traced by the carbon monoxide (with its isotopologues) via its radio emission line at 2.6 mm. However, since the CO is freezing out on grains at very high densities, other tracers such as HCN or N2H+ can be used to probe denser regions such as prestellar cores. To date, over 150 molecules have been uniquely identified in the ISM 7 .

Finally, as we will see in the next sections, the presence of hydrogenated polycyclic aromatic molecules (PAHs) ubiquitously observed in emission in the infrared range through the infrared emission features [START_REF] Leger | Identification of the 'unidentified' IR emission features of interstellar dust?[END_REF]Allamandola et al. 1985;[START_REF] Peeters | The rich 6 to 9 vec mu m spectrum of interstellar PAHs[END_REF] and the recent discovery of the fullerenes (C 60 , C 60 + , C 70 ) in regions with intense UV radiation such as planetary nebulae (Cami et al. 2010;[START_REF] García-Hernández | Formation of Fullerenes in H-containing Planetary Nebulae[END_REF], or reflection nebulae (Sellgren et al. 2010;Berné et al. 2013a) but also in the diffuse ISM [START_REF] Berné | Detection of buckminsterfullerene emission in the diffuse interstellar medium[END_REF] herald the emergence of a new era in the exploration for complex molecules in space.

Dust

Dust grains represent the solid phase of the interstellar medium. They are mainly small solid particles whose dimensions range from nanometer (10 -9 m) to micron (10 -6 m) (smoke particles). These grains are composed essentially of carbon, oxygen, silicon, iron and generally surrounded by a thin envelope of water ice and ammonia. They are composed of either hydrogenated amorphous carbon or silicates, in other words, soot and sand and sometimes they form aggregates to form larger clumps of grains in dust clouds. Dust does not form in the interstellar medium because it is too tenuous for many molecules to form there. It forms in the vicinity of endof-life stars when huge amounts of hot matter (atoms, small molecules and clusters) are ejected, either as a stellar wind or via an explosive supernova.

Although their mass represents typically only ∼ 1% of the interstellar matter, dust grains play a critical role in physical and chemical conditions of the ISM:

-Dust is intimately linked to gas and is the main source of environmental heating via the photoelectric effect.

1.6. The Diffuse Interstellar Bands follows:

I(λ) = I 0 (λ) e -τ λ
(1.1)

A λ = -2.5 log( I(λ) I 0 (λ) ) = 1.086 τ λ (1.2)
where τ λ is the optical depth.

For any observation, one must take into account the effect of the interstellar extinction. In the case of a star of absolute magnitude M at the distance d, the apparent magnitude is given by:

m(λ) = M(λ) -5 + 5log(d) + A λ (1.
3)

The color of the star is also changed in the presence of a cloud. Radiation is more absorbed in blue than in red, A λ varies like λ -1 in the optical range.

The standard excess color is the difference between the magnitudes in the large bands B (blue) at 440 nm and V (visible) at 550 nm.

E(λ -V) = (A λ -A V ) (1.4)
The extinction curve is defined by R V =A V /E(B -V), the ratio of total to selective extinction at V, or E(λ -V)/E(B-V) [START_REF] Cardelli | The relationship between infrared, optical, and ultraviolet extinction[END_REF]. Figure 16 shows the interstellar extinction curve from the far-IR through the UV region centered at 2175 Å (UV-bump). On the average extinction curve of the interstellar medium of our Galaxy, R V = 3.1. However, deviations from this value (R ranges between about 2.2 and 5.8) were observed and then refers to a so-called abnormal extinction (e.g. [START_REF] Chini | Abnormal extinction and pre-main sequence stars in M 16 (NGC 6611)[END_REF]. Note that R V is a powerful tool to measure the dust grain size.

The Diffuse Interstellar Bands

The spectra of stars viewed through one or several interstellar clouds display a plethora of relatively weak non-stellar absorption features of unknown origin, the so-called Diffuse Interstellar Bands. The first published reference to the DIBs goes to Annie J. Cannon who has misidentified the strongest DIB at 4430 Å as H γ in her 1890s survey of stellar spectra (Code 1958). Soon followed, the documented discovery of DIBs at 5780 and 5797 Å around the early 20's by Mary lea Heger. Their interstellar origin was established in the 1930s [START_REF] Merrill | Unidentified Interstellar Lines // PASP[END_REF][START_REF] Merrill | Stationary Lines in the Spectrum of the Binary Star Boss 6142[END_REF] on the basis of two findings: (1) the stationary bands nature as observed in the velocity variations of spectroscopic binaries (2) their strength increase with distance and with the degree of reddening.

Introduction

Only the DIBs at 5780, 5797, 6270, and 6283 Å were known before [START_REF] Herbig | The diffuse interstellar bands. IV -The region 4400-6850 A[END_REF] identified 39 new ones in the [4400-6850] Å range. Today, more than 400 optical DIBs (400-900 nm) are known (e.g. [START_REF] Jenniskens | A survey of diffuse interstellar bands (3800-8680 A)[END_REF]Hobbs et al. 2009). There are no firm detections in the near-UV [START_REF] Bhatt | A Sensitive Spectral Survey of Interstellar Features in the Near-UV [3050-3700 Å[END_REF]. The first detection of near-infrared (NIR) DIBs was reported by Joblin et al. (1990) near 11 800 Å and 13 200 Å . Currently, ∼30 NIR DIBs have been identified (Foing, Ehrenfreund 1994;[START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF]Cox et al. 2014;[START_REF] Hamano | Near-infrared Diffuse Interstellar Bands in 0[END_REF][START_REF] Hamano | Near Infrared Diffuse Interstellar Bands Toward the Cygnus OB2 Association[END_REF]. NIR DIBs are particularly useful since they allow to make use of highly reddened target stars and explore the densest areas of the ISM. Most measured DIBs have a Galactic origin. Still, they have also been detected in the Magellanic clouds, M 31, and M 33 (Ehrenfreund et al. 2002;Welty et al. 2006;Cordiner et al. 2008aCordiner et al. ,b, 2011;;van Loon et al. 2013) and in a few line-of-sights toward starburst galaxies or in Type Ia supernovae spectra, for instance [START_REF] Heckman | The Detection of the Diffuse Interstellar Bands in Dusty Starburst Galaxies[END_REF]Sollerman et al. 2005;Cox, Patat 2008;[START_REF] Phillips | On the Source of the Dust Extinction in Type Ia Supernovae and the Discovery of Anomalously Strong Na I Absorption[END_REF]. A DIB radial gradient was established for the first time in a 160 Mpc distant galaxy (Monreal-Ibero et al. 2015).

There is a huge litterature devoted to the DIBs and the DIB-related topics.

Excellent review papers by Herbig (1995); Sarre (2006), a comprehensive historical review by McCall, Griffin (2013) and the excellent contributions that are published in the proceedings of the DIB-dedicated symposia by Cox, Cami (2014). In the following, we focus on the problem from an observational and spectroscopic angle.

Observational properties

We can make some general comments on the different properties of DIBs based on what we have learned from the astronomical data:

DIBs distribution: Where do we find the DIBs?

• DIBs are seen in the spectra of stars, nearby galaxies such as the Magellanic Clouds (see e.g. Ehrenfreund et al. 2002;Cox et al. 2007b) and the Andromeda Galaxy (M31) (Cordiner et al. 2011, and references therein), distant galaxies (Monreal-Ibero et al. 2015), and quasars (towards several sightlines) (e.g. Lan et al. 2015).

• DIBs are spanning the wavelength range from the near ultraviolet (UV) at λ 4000 Å to the near infrared (IR) at λ 18 000 Å (Fig. 17) which is equivalent to photon energies in the 1-3 eV range. In the UV part, no DIB has been detected (e.g [START_REF] Seab | A search for diffuse interstellar bands in the ultraviolet[END_REF][START_REF] Snow | The Diffuse Interstellar Bands in History and in the UV // EAS Publications Series[END_REF].

• Most of DIB observations concern the diffuse (atomic or molecular) ISM. To date, no circumstellar DIB has been found with the possible exception of some detected features toward the planetary nebulae Tc 1 [START_REF] Díaz-Luis | A search for diffuse bands in fullerene planetary nebulae: evidence of diffuse circumstellar bands[END_REF].

1.6. The Diffuse Interstellar Bands

• DIBs are not detected in emission except for the very intriguing case of the Red Rectangle [START_REF] Sarre | Diffuse bands in emission[END_REF][START_REF] Van Winckel | Why is the Red Rectangle Unique? // The Diffuse Interstellar Bands[END_REF].

• In a general sense, atoms, molecules, and DIBs are spatially grouped based on the study of their radial velocities. Still, minor kinematic disparities exist among all them (e.g. [START_REF] Sonnentrucker | Distribution of gas, dust and the lambda 6613 Å DIB carrier in the Perseus OB2 association[END_REF].

Figure 17 The strongest identified DIBs seen towards an optical-IR spectrum. Adapted from [START_REF] Jenniskens | A survey of diffuse interstellar bands (3800-8680 A)[END_REF]. Credit: Gail Zasowski.

Appearance, shape and profiles

• DIBs are broader than atomic or diatomic interstellar lines 8 (hence the label diffuse) and show a wide range of widths and intensities with full widths at half maximum (FWHMs) ranging from ∼ 0.4 to ∼ 40 Å (for e.g. Hobbs et al. 2009).

• DIBs show a wide range of intensities but are not saturated for most of the distant star spectra of the Galaxy.

• Most of DIBs are asymmetric with longward or shortward wings (which often makes it difficult to determine the central position in wavelength).

• Some DIBs (often the narrowest ones) show PQR branches indicating the gas-phase molecular nature of the carriers (hence the label band).

Figure 18 illustrates the case of the DIB at 6614 Å.

• The intrinsically9 broad DIBs (FWHM ≥ 6 Å) such as the 4430 Å band are very strong, free of any internal structure and are known to be most consistent with a Lorentzian distribution [START_REF] Snow | Unusually Weak Diffuse Interstellar Bands toward HD 62542[END_REF] or multiple Gaussian modeling [START_REF] Sonnentrucker | A Modern Census of the Broadest Diffuse Interstellar Bands[END_REF].

Correlations

• Inter-DIB correlations: DIBs do not correlate well with each other except for the NIR DIBs (Cox et al. 2014) and the well-known 6196 and 1.6. The Diffuse Interstellar Bands positively or negatively correlated with H 2 (Lan et al. 2015). Some DIBs also correlate with small molecules and radicals.

DIB families

• DIBs are not of common origin. Hence, it is possible to classify DIBs into sets or families. The members of a particular family reveal similar patterns of strength fluctuation among various different lines of sight, while they have weak correlations with the members of other groups, i.e. the strength ratios of DIBs belonging to the same family are supposed to be approximately constant and correspond to carriers sharing the same properties. The ratio between the 5780 Å and 5797 Å DIBs is generally used as benchmark to differentiate the families.

Based on this behavior, classifications that categorized some DIBs into three groups have been first introduced by [START_REF] Krelowski | Three families of diffuse interstellar bands?[END_REF]; [START_REF] Westerlund | The division of diffuse interstellar bands into families[END_REF].

• Some of DIBs correlate with the column density of C 2 molecules to which they appear to be chemically related. This family is known as C 2 -DIBs (Thorburn et al. 2003).

• The DIB spacing has been much studied in a search for a significant pattern but except for a set of weak DIBs regularly spaced near 5778 or 6283 Å DIBs [START_REF] Jenniskens | Complex Structure in Two Diffuse Interstellar Bands[END_REF], the separation of about 20 cm -1 in pairs of some C 2 -DIBs (Thorburn et al. 2003), the detection of a sequence of a weak spaced bands between 6770-6865 Å [START_REF] Herbig | The diffuse interstellar bands. VI -New features near 6800 A[END_REF][START_REF] Krelowski | Groupings of Strong and Weak Diffuse Interstellar Bands Formed by Common Carriers[END_REF], and within the 5910-5990 Å range [START_REF] Bondar | A Possible New Sequence of DIBs // The Diffuse Interstellar Bands[END_REF]) and an analysis of vibronic progressions in DIBs (Duley, Kuzmin 2010), no fully convincing spacenumber has been reported thus far (see e.g. Hobbs et al. 2009). Finding regularities is interesting insofar as it could be an indication of a spectroscopic signature such as a signature of the spin-orbit interaction in a linear molecule.

Environmental dependence

• DIBs do not have identical strength ratios towards different lines of sight. It was demonstrated that the mutual correlations and strengths depend on physical conditions, extinctions curves and abundances of the interstellar environment(e.g. [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF][START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Cox, Spaans 2006). The 5780/5797 ratio has been connected to the environment through the well-known classification into σ-Sco and ζ-Oph type clouds. Sigma-Sco cloud extinction curves are characterized by a pronounced 2175 Å bump and strong FUV rise, whereas ζ-Oph type cloud is showing an extinction curve with weak 2175 Å bump and weak rise of the FUV extinction (Fitzpatrick, Massa 2007). In fact, the 5797 Å DIB is well correlated in ζ-type sightlines while • The formation/destruction of the DIB carriers is at least partially controlled by the UV radiation field [START_REF] Ruiterkamp | PAH charge state distribution and DIB carriers: Implications from the line of sight toward ¡ASTROBJ¿HD 147889¡/ASTROBJ¿[END_REF]Cox, Spaans 2006) .

• DIBs are controlled by the environmental conditions at parsec scales (e.g. Vos et al. 2011), and even at very small, hundreds of A.U. scales as shown by (e.g. [START_REF] Cordiner | Discovery of small-scale-structure in the large molecule/dust distribution in the diffuse ISM[END_REF].

Tracers

• Based on σ and ζ types, The 5780/5797 ratio can be used as a tracer indicating the presence of simple diatomic molecules such as CO, CH, C 2 or an influence of the UV radiation field (see for e.g. [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Friedman et al. 2011). In other words, the 5780 Å DIB may trace atomic gas of diffuse clouds while the 5797 Å DIB will be associated to the dense molecular regions. The 5797/5780 DIB ratio is related to the effective UV radiation field strength; UV exposed environments, σ-type, have ratios < 0.35, while UV-shielded, ζ-type environments, have ratios ≥ 0.35 (Vos et al. 2011).

• A major advantage of DIBs over extinction is the measurement of their Doppler shifts, which will make it possible to attribute movement to interstellar complexes and locate those responsible for radio-milimetric emission lines in the frame of the 3D ISM mapping (see Puspitarini et al. 2015).

DIB carriers

While [START_REF] Russell | The analysis of spectra and its application in astronomy[END_REF] and [START_REF] Swings | A note on molecular absorption in interstellar space[END_REF] proposed large molecules as DIB carriers, Merrill, Wilson on the other hand proposed dust grain particles to be responsible of DIBs. Since then, there has been a considerable debate about whether species giving rise to the observed DIBs are associated with small solid particles or gas-phase molecules. However, thanks to observational progress a consensus has been reached over the last ten years on their nature: DIB carriers are now thought to be large carbonaceous molecules (≥ 10-100 atoms) in the gaseous phase but other possibilities (such as noncarbon species) remain open as discussed by Snow (2014).

There are strong arguments working against the solid phase hypothesis. In the case of solid-sate, what would cause an absorption like the one observed would be the presence of impurities. But, since DIBs fall at constant wavelengths this would imply the need for exactly the same environment in the solid everywhere along its surface in all clouds and all conditions, which is 1.7. DIB carriers highly improbable. As a second strong argument, searches for polarization of the DIB profiles have set stringent upper limits to the polarization efficiency of the carriers. This indicates that a carrier is not attached or part (an impurity) of the dust grains causing the continuum polarization. An additional evidence for the molecular nature of the DIBs carriers is supported by the detection of sub-structures in the line profiles of some DIBs that are extremely similar to PQR branches (rotational contours) of complex gas phase molecules (Sarre et al. 1995;Ehrenfreund, Foing 1996;[START_REF] Krełowski | Intrinsic Profiles of Strong Diffuse Interstellar Bands[END_REF][START_REF] Walker | Profiles of the λ6196 and λ6379 Diffuse Interstellar Bands[END_REF]Cami et al. 2004). Finally, A number of them have a width significantly greater than 1 Å, which excludes small molecules as possible carriers. They could therefore be due to electronic transitions in large molecules.

The most promising carrier candidates are carbon chains, polycyclic aromatic hydrocarbons (PAHs), and fullerenes. A large effort has been devoted over the years for the identification of the DIB carriers. Searches have included PAH cations (Salama et al. 1999;Bréchignac, Pino 1999), neutral PAHs (Salama et al. 2011;Gredel et al. 2011), carbon chains (Motylewski et al. 2000;Maier et al. 2004), and H 2 (for e.g. Sorokin, Glownia (1999) and have not resulted in the identification of a specific carrier. The only identification came recently for the case of the fullerene cation, C 60 + (Campbell, Maier 2018, and references therein), providing a link between some DIBs and organic polyatomic molecules and ions. [START_REF] Jones | Dust evolution, a global view: II. Top-down branching, nanoparticle fragmentation and the mystery of the diffuse interstellar band carriers[END_REF] proposed a top-down branching model for DIB carrier formation. Figure 19 shows the hierarchical scheme of such tree branching with descending in size and in chemical variety from top to bottom. In such scenario, dehydrogenated, ionized, hetero-cyclic, olefinic and aromatic-rich moieties, that are formed by fragmentation of the large grains or nanoparticles, are believed to carry the DIBs. In what follows we briefly recall the main findings for the most popular candidates: PAHs and fullerenes.

DIB carriers: PAHs?

Infrared emission bands In 1973, Gillett et al. observed for the first time a set of emission bands between 8 and 13 µm in the reflection nebula NGC 7027. The emission bands have been detected in many different astrophysical environments (such as the photodissociation regions, HII regions, and nebulae planetary) especially thanks to the ISO satellite, the Spitzer Space Telescope and then AKARI. The strongest signatures can be found at 3.3, 6.2, 7.7, 8.6, 11.3 and 12.7 µm as shown in Fig. 111 (for a recent review, see [START_REF] Tielens | Interstellar Polycyclic Aromatic Hydrocarbon Molecules[END_REF]. The bands were first called "unidentified infrared bands" or "unidentified infrared features" [START_REF] Willner | Unidentified infrared spectral features[END_REF] and their origin remained unknown for a decade. But from the moment these bands were combined to the stretching and bending (C-H and C-C) vibration modes characteristic of 1. Introduction Figure 19 Top-down branching scenario of DIB formation and fragmentation as imagined by [START_REF] Jones | Dust evolution, a global view: II. Top-down branching, nanoparticle fragmentation and the mystery of the diffuse interstellar band carriers[END_REF]. DIB carriers may be formed by fragmentation of large grains, nanoparticles (and smaller) or fragments of dehydrogenated radical and molecular ions. With the destruction of the latter by UV-photoprocessing, non-DIB species and isolated atoms and ions will be formed in the gas. Only the configurations that lead to the most stable fragments are shown here. Taken from the tome II of "Dust evolution, a global view" by [START_REF] Jones | Dust evolution, a global view: II. Top-down branching, nanoparticle fragmentation and the mystery of the diffuse interstellar band carriers[END_REF]. aromatic hydrocarbons [START_REF] Duley | The infrared spectrum of interstellar dust -Surface functional groups on carbon[END_REF][START_REF] Leger | Identification of the 'unidentified' IR emission features of interstellar dust?[END_REF]Allamandola et al. 1985) comes the new name of Aromatic Infrared Bands (AIBs) 11 . Despite the numerous works, both in molecular physics, Laboratory and astrophysics, no single PAH molecule or related small complex has been firmly identified to date, either by rotational spectroscopy or by observations of the associated infrared emission bands. It should be mentioned that the emission bands only indicate the general class of molecule, and not the specific molecular species [START_REF] Joblin | PAHs and the Universe: A Symposium to Celebrate the 25th Anniversary of the PAH Hypothesis[END_REF], for a recent review). However, to date, PAHs remain the best candidates to explain infrared bands in emission. derivatives, existing in different sizes and shapes and in ionized and/or dehydrogenated forms.

PAHs as DIB carriers

• Being broader, it is difficult to reliably extract PAH ions bands from astronomical data. There is a need for a depth-analysis to search for the signature of specific PAHs and derive reliable column densities (or upper limits to column densities) specially in the UV range that were being marginally explored. By providing enough constraints on DIBs, this can limit the number of possible candidates to study in the laboratory (see the excellent critical review by Salama, Ehrenfreund 2014). Finally, since the detection of fullerenes (subject of the next section) in 2010 and the very recent first definitive detection of the rotational emission from the cyclic aromatic benzonitrile c-C 6 H 5 CN by [START_REF] Mcguire | Detection of the aromatic molecule benzonitrile (c-C 6 H 5 CN) in the interstellar medium // Science[END_REF] in an interstellar cloud12 using new data from the National Science Foundation's Green Bank Telescope (GBT), the existence of large carbon-related molecular buildings has become a reality, which also illustrates the carbon-richness and chemical complexity of this environment (see [START_REF] Joblin | Detecting the building blocks of aromatics[END_REF].

This gives a new breath to the PAH model and demonstrates the need to make all necessary improvements before excluding PAHs from the DIB carriers candidates. 1.7. DIB carriers 1.7.2 DIB carriers: Fullerenes? Fullerenes In 1985, Kroto et al. discovered a new form of carbon that could be present in the astrophysical environments 13 . A molecule composed of 60 carbon atoms and arranged into what look like the geodesic domes of the architect R. Buckminster Fuller. Subsequently, these molecules were called buckminstefullerene14 or buckyballs due to their soccer-ball-shape. However, two questions remained unresolved: does C 60 and its derivatives exist in the ISM? Can we linked these large molecules to the DIBs?

A positive answer to the first question was given by the detection of the IR emission bands (at 7.0, 8.5, 17.4, and 19.0 µm for C 60 ) in the planetary nebulae Tc 1 (IC 1266) (Cami et al. 2010). These signatures match the IR active bands of the neutral fullerenes C 60 and C 70 detected in the laboratory by Kroto and coworkers. More additional detections were repertord in different environments such as the reflection nebulae (NGC7023 Sellgren et al. 2010) and the proto-planetary nebula IRAS01005+7910 [START_REF] Zhang | Detection of C 60 in the Protoplanetary Nebula IRAS 01005+7910[END_REF]. In 2013, Berné et al. provided a first proof of the presence of the fullerenes in its ionized form by its mid-infrared (MIR) emission in the reflection nebula NGC 7023. Foing, Ehrenfreund (1994) found that the laboratory spectrum of the buckminsterfullerene cation C 60 + in argon and neon matrices [START_REF] Fulara | Laboratory evidence for highly unsaturated hydrocarbons as carriers of some of the diffuse interstellar bands[END_REF]) would exhibit transitions near the two NIR DIBs at 958 and 963 nm. Additional DIBs at 902, 921, and 926 nm were attributed to C 60 + [START_REF] Misawa | Identification of New Near-Infrared Diffuse Interstellar Bands in the Orion Nebula[END_REF]). The detection of the two strong bands has been confirmed by several teams (Foing, Ehrenfreund 1997;Galazutdinov et al. 2000;Cox et al. 2014;[START_REF] Hamano | Near-infrared Diffuse Interstellar Bands in 0[END_REF][START_REF] Hamano | Near Infrared Diffuse Interstellar Bands Toward the Cygnus OB2 Association[END_REF]Walker et al. 2015Walker et al. , 2016[START_REF] Walker | The 9577 and 9632 Å Diffuse Interstellar Bands: {{\rm{C}}} {60}ˆ{+} as Carrier[END_REF]Lallement et al. 2018b) and some detections of the weaker absorption bands have been reported (Walker et al. 2015(Walker et al. , 2016)). However, this identification has long been debated [START_REF] Maier | Interstellar detection of C 60 +[END_REF][START_REF] Jenniskens | Diffuse interstellar bands near 9600 Å: not due to C60plus yet[END_REF]. The main cause of this questioning is that the production of a laboratory spectrum in conditions similar to those of DIB formation (gas-phase with low temperatures) has proven elusive.

DIB-Fullerenes

Nevertheless, the credibility of the DIB-C 60 + assignment has increased with the laboratory work of the John P. Maier's team. Campbell et al. (2015) had reported a striking match of five DIBs with transitions of the laboratory spectrum of internally cold C 60 + ions. The measurement was achieved with C 60 + 13 Historical note: This discovery earned them the Nobel Prize in Chemistry in 1996. Prior to this work, the existence of C 60 has been predicted by D.E.H. Jones (under his pseudonym Daedalus) in New Scientist, 1966, and with some works in 1970 by E. Osawa, and independently by D.A. Bochvar and Gal'pern 1. Introduction molecules in the gas phase, cooled to 5.8 kelvin. Subsequently, in the face of some rejections (Galazutdinov et al. 2017;Galazutdinov, Krełowski 2017), several laboratory investigations supported this approach by providing additional precisions under appropriate conditions (Campbell et al. 2016a,b;Kuhn et al. 2016;[START_REF] Spieler | {{{\rm{C}}} {60}}ˆ{+} and the Diffuse Interstellar Bands: An Independent Laboratory Check[END_REF] and the most recent improvements can be found in Campbell, Maier (2018).

In the frame of astronomical data, it is very challenging to reveal the existence (or lack) of such features from IR ground spectra only as this wavelength range is strongly contaminated by telluric absorption. It requires a careful telluric absorption correction as illustrated by Lallement et al. (2018b). Through dedicated corrections and stacking methods applied to EDIBLES spectra, some substructures in the 9577 Å C 60 + band were detected (see Cami et al. 2018). On the other hand, since the problem can be avoided by using free-telluric data from space-based telescopes, there may be a new avenue in exploring the newly opened NIR window with the Space Telescope Imaging Spectrograph (STIS) on board the Hubble Space Telescope (HST) (Cordiner et al. 2017a).

Thesis Overview

Understanding the evolution of interstellar matter and the physical and chemical processes that govern the early stages of star formation is part of a continuous effort by much of the astrophysical community to answer fundamental questions about the origin of life in the Universe. To do this, it is necessary to acquire a precise knowledge of the composition and life cycle of gas and dust clouds in the interstellar medium. Among the interstellar materials present in space, carbon-based matter under the form of large molecules is of crucial importance since it is part of the chain of processes at work from cloud cooling and collapse to protostars and their protoplanetary systems. Indeed, carbon-based molecules of increasing sizes are detected in comets as missions progress, potentially representing pristine interstellar material. So the study of the formation, processing and destruction of large carbon-bearing species is fundamental.

The composition of interstellar matter and the processes involved are far from being fully understood. In particular, one of the remaining problems in terms of ISM composition is the ubiquitous presence of the many unidentified diffuse interstellar bands detected in our galaxy and other galaxies. These absorption features observed in the spectra of reddened Milky Way stars or in galaxy spectra have puzzled astronomers and spectroscopists for over a century. Since their first discoveries in the 1920s, there has been considerable debate about whether the molecules responsible for the observed DIBs are associated with dust grains or gas-phase molecules. However, 1.8. Thesis Overview thanks to the progress of observations, a consensus has emerged over the past ten years on their nature: carriers of DIB are now considered as large carbonaceous molecules in the gas phase. No polarization has been detected, and direct evidence of the molecular nature of DIB carriers is supported by the detection of substructures in the linear profiles of some DIBs indicative of rotational contour bands of complex molecules in the gas phase. The most promising candidate carriers are carbon chains, polycyclic aromatic hydrocarbons (PAHs) and fullerenes. PAHs are the most favoured species, being ubiquitously observed in IR emissions. Due to their stability, fullerenes are also good candidates, and C 60 , C 60 + and C 70 have also been detected in emission. The only recent identification of the fullerene cation case, C 60 + , provides a link between some DIBs and polyatomic organic molecules and ions.

Further progress in DIB research will undoubtedly come from an active synergy between theoretical work, astronomical observations and laboratory measurements (UV and IR) where the interstellar conditions prevailing in the diffuse ISM can be realistically simulated. This methodology is now at the heart of the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) in which observing astronomers, laboratory astrophysicists and chemists have joined forces to acquire a new understanding of the physics of DIB carriers.

Until recently, most DIB studies of astronomical spectra had as their main objective the identification of carriers by means of high quality measurements, and therefore focused on a limited number of hot, bright, strongly reddened stars, using increasingly powerful instruments. This thesis corresponds to a shift towards statistical studies: such a trend is made possible and motivated by the increasing number of stellar spectroscopic studies with high multiplex instruments. It is now possible to collect massive amounts of data, both in terms of the number of target stars and of DIBs observed simultaneously. This has paved the way for new types of studies. This thesis presents a large number of DIB extractions from stellar spectroscopic surveys and three of these new analyses:

Statistical studies on the relationship between DIB's strengths and the physical properties of their hosting clouds.

Tomographic studies of DIB carriers on small and large spatial scale.

Extraction of line profiles based on carefully selected sightlines and single-cloud spectra stacking, studies of the profile substructures and spatial variability. • Part II, Chapters 4 and 5 -Applications of the DIB catalogs.

Chapter 4 -3D ISM mapping

Despite the absence of identifications, optical or NIR DIBs are interstellar cloud tracers and we have tested their use for 3D mapping. We merged the catalogue of DIBs derived from Tycho targets with the extinction data, using a conversion factor based on our DIB-extinction correlation study and taking into account the dispersion around the mean relationship. The merged dataset was inverted to produce a 3D map of the local ISM presented in Capitanio et al. (2017). We also used the latest APOGEE catalog of 15 273 Å DIB for the first 3D inversion based solely on DIBs and compared the 3D map obtained with the one obtained by the extinction for exactly the same targets.

Chapter 5 -Applications under study of the skin effect

It has been shown that some of the strong optical DIBs level off with respect to dust reddening (visual extinction) for lines of sight that probe (on average) the denser cores, rather than the diffuse edges, of interstellar clouds. This is called the "skin effect" and the effect is attributed to the influence of the radiation field on the DIB carriers, through charge state variation or formation/destruction processes, or independently to accretion processes. The link with the ionizing field is particularly interesting because it is related to the shielding and thus the clouds depth, and therefore to the properties of the grain. In addition, since near-infrared DIBs can probe more strongly obscured areas, and therefore cores of denser clouds, they are particularly interesting in the context of the skin effect. In this chapter, the skin effect is explored using our APOGEE DIB measurements, in combination with existing, precise estimates of dust extinction for the APOGEE targets. Planck measurements of the optical depth of Galactic dust are also extrapolated from Planck maps for all APOGEE target directions. The ratio between the column of DIB carrier and the extinction (grain column) is found to be strongly negatively correlated with the optical depth of dust in the case of the nearby molecular clouds seen off-Plane and This work has led to the production of catalogues, the characterization of very weak DIBs that were previously barely detected in a few lines of sight, and the detection of new candidates.

• Part IV: Chapter 7 -Tomography of a DIB carrier in a single dark cloud

In the frame of a pilot project with VLT/KMOS (ESO 096.C-0931(A), PI: N. Cox), moderate resolution near infrared spectra (H-band) of 85 stars located behind the dark globule Barnard 68 were recorded. In this chapter, I detail how the data were adjusted by the convolved product of a telluric transmission and a stellar model. The telluric spectrum is a TAPAS transmission adjusted according to the air mass and the observation site. The stellar model was built from the SDSS/APOGEE stellar library. Based on a simplified inversion assuming sphericity, and using the edge-to-center distribution of the targets, it was found that the volume density of the 15 273 Å DIB carrier is 2.7 (resp. 7.9) times lower than the average value in the external (resp. central) regions of the cloud (n(H) of 3.5 x 10 5 cm -3 ). This is the first DIB tomography in an individual cloud.

• Part V: Chapters 8, 9, and 10 -The EDIBLES survey

The VLT/UVES EDIBLES survey is a unique collection of very high signal to noise (reaching 800-1500), high spectral resolution and large 1. Introduction spectral coverage spectra recorded towards more than 100 carefully selected targets. Chapter 8 contains the presentation of the survey. Our contribution corresponds to specific data treatments, including telluric line removal.

Chapter 9 describes the characterization of the line-of-sight cloud structure of EDIBLES targets. This is a mandatory step, as a number of DIB studies can only be interpreted for lines of sight crossing a single dominant cloud. To this end, several gaseous lines were fitted, in particular using new automated fitting methods. The spectra from the same target recorded at different periods were stacked in a fixed reference frame in order to increase the signal to noise (we choose the stellar frame for facility) . Then we selected the seven targets for which only one interstellar cloud is majoritarily producing the absorption (the single-cloud situation). We stacked their spectra in the stellar, telluric and cloud frame respectively and compared the results with the goal of obtaining the best DIB profiles and identifying telluric and stellar potential contaminations.

Chapter 10 describes how EDIBLES data have made it possible to characterize in an unprecedented way the spectral profiles of the main DIBs and the first in-depth study of the low DIBs associated with the C 2 molecule in dense cores. All these DIBs have profiles characterized by substructures, with clear variability from one DIB to another and with some consistent profile changes depending on the line of sight. Such information will be available as a guide for future experimental work carried out under conditions similar to those in the ISM.

Abstract

High resolution stellar spectroscopic surveys provide massive amounts of diffuse interstellar bands (DIBs) measurements. Data can be used to study the distribution of the DIB carriers and those environmental conditions that favor their formation. In parallel, recent studies have also proved that DIBs extracted from stellar spectra constitute new tools for building the 3D structure of the Galactic Interstellar Medium (ISM).

The amount of details on the structure depends directly on the quantity of available lines of sight (LOS). Therefore there is a need to construct databases of high-quality DIB measurements as large as possible. We aim at providing the community with a catalog of high-quality measurements of the 1.5273 µm DIB towards a large fraction of the Apache Point Observatory Galactic Evolution Experiment (APOGEE) hot stars observed to correct for the telluric absorption and not used for ISM studies so far. This catalog would complement the extensive database recently extracted by Zasowski et al. (2015) from the APOGEE observations and used for 3D ISM mapping. We devised a method to fit the stellar continuum of the hot calibration stars and extracted the DIB from the normalized spectrum. Severe selection criteria based on the

Introduction

In the near-IR, the 1.5273 µm band (hereafter, we will also use 15273 Å and refer to vacuum wavelengths) falls in the spectral range observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE, Wilson et al. 2010;Majewski 2012), and this high resolution, high signal, massive survey was used by Zasowski et al. (2015) to trace the 3D structure of the ISM at the large scale based on an unprecedented number of extracted NIR DIBs. Such pioneering work has demonstrated the potential of these methods and consequently that increasing the number of measurements will result in more accurate mapping.

In this chapter we present a work based on a fraction of the spectra of the APOGEE's target from the Data release 12 (DR12) made publicly available. The entire dataset comprises ∼160 000 stars, most of them cool K and M giants (T e f f ∼ 3500 -5000 K). As part of the observing strategy and calibration plan of the survey, ∼17 000 hot telluric stars were also observed (see details in Sec. 2.2). By definition these are the bluest stars on a given pointing, and thus, most of the times early (i.e. O-to F-type) stars. As such, their almost featureless continua make them ideal targets to extract the information associated to the 1.5273 µm DIB. Our work has been focused on these calibration stars. We have devised a method to fit their stellar continua and we present the resulting catalog of DIB measurements extracted from their spectra. This catalog complements the existing extensive catalog of Zasowski et al. (2015) based on the APOGEE main, cool targets.

The chapter is structured as follows: Sect. 2.2 contains the technical aspects regarding the data used in this work. Sect. 2.3 describes the methods used for continuum fitting and DIB parameter extraction, whereas a detailed description of our criteria to validate a DIB detection and estimate uncertainties is included in Sect 2.4. Sect. 2.5 describes the resulting catalog and its validation. Conclusions are summarized in Sect. 2.6.

The data

The data

APOGEE overview

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) is one of the four Sloan Digital Sky Survey III (SDSS-III, Eisenstein et al. 2011;[START_REF] Aihara | [END_REF]) experiments operated from 2011 to 2014. APOGEE uses a 300-fiber multi-object spectrograph working in the near infrared (H-band, 1.51-1.70 µm) at high spectral resolution (R∼22 500, Wilson et al. 2010). The spectrograph is attached via a fiber optic train to the SDSS 2.5-meter telescope at Apache Point Observatory [START_REF] Gunn | The 2.5 m Telescope of the Sloan Digital[END_REF].

Since the effects of extinction at near-IR wavelengths are reduced, APOGEE has a strong potential to explore dust obscured regions of the Galaxy that are beyond the reach of optical surveys, in particular the inner Galaxy and bulge.

In a given observation, about 230 (out of 300) fibers are allocated for scientific targets selected from the source catalog the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006).

The ∼160 000 selected scientific targets are distributed across all Galactic environments. Most of them are red giant stars but there are also special subsamples including stars with measured parameters and abundances from other spectroscopic studies, cluster members, etc. associated to several APOGEE ancillary science programs (see Zasowski et al. 2013, for a detailed description of the different target classes).

In addition to the science targets, a sample of hot stars, also called telluric standards stars (TSSs) or hot telluric calibrators are observed (35/300 fibers) to allow for the correction of the telluric absorption lines (see below). Finally, the remaining ∼35 fibers are allocated for observing blank sky positions to enable sky emission subtraction.

The APOGEE telluric standard stars

The total number of TSSs in the APOGEE survey is nearly ∼ 17 000. Ideally, for a good telluric correction one would like to have bright O, B and A stars because of their featureless spectra. Therefore, to maximize the chances of observing stars of these types, the TSSs were selected as the bluest stars on a given plate having a magnitude in the range 5.5≤H≤11 and ensuring a uniform distribution over the plate (see Zasowski et al. 2013, for details of TSS selection). Their spectra are used to adjust in an exquisite way the atmospheric transmission and its variability throughout the field, a transmission that is used to remove the telluric lines in all observed targets as illustrated in Fig. 21.

Data analysis

Server (SAS) as described on the data access page1 . There are three different types of reduced spectra available (see Nidever et al. 2015;Holtzman et al. 2015). Here, we use the calibrated, well-sampled, and pseudo continuumnormalized combined 1D spectra. These are available as fits files with four separate extensions: the first one carries the information about the star and the derived stellar parameters, the second one contains the observed spectra, the third one saves the error pixels and the fourth one has a state-of-the-art modeled spectrum S λ adjusted to the observed one (García [START_REF] Pérez | ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline[END_REF]. The modeled spectra are interpolated from a grid of spectra, itself based on a large sample of model atmospheres (Mészáros et al. 2012). These modeled spectra are optimized for stars at temperature 3 800 K< T < 5 250 K (Ahn et al. 2014). The TSSs are a priori hotter, and therefore as shown below, small adjustments to the provided modeled spectra were needed.

All the spectra are sampled at same rest wavelength pixel scale, with a constant dispersion in log λ, as

log λ i = 4.179 + 6 × 10 -6 i (2.1)
with 8575 total pixels (i = 0 to 8574), giving a rest wavelength range of 15 100.8 to 16 999.8 Å (Nidever et al. 2015).

We also use the table that summarizes all the parameters derived from the APOGEE spectra (for DR12, allStar-v603.fits), including for each observed individual star its mean barycentric radial velocity, the standard deviation of the mean velocity, the mean ASPCAP parameters and abundances as derived from the combined spectra, and a compilation of ancillary targeting data (see Nidever et al. 2015;Holtzman et al. 2015, for a full description of the data in these files).

Data analysis

The aim of this work is analyzing the series of ∼16 000 DR12 TSS spectra that can be downloaded from the APOGEE site to determine the presence (or absence) of the DIB at 15273 Å and measuring its equivalent width in case of positive detection.

Given the volume of data, this needs to be done with as less as possible human intervention.

For that, we took as starting point the methodology presented by [START_REF] Puspitarini | Automated measurements of diffuse interstellar bands in early-type star spectra[END_REF] for optical spectra, and adapted it to the peculiarities of APOGEE spectra. At the end, we had a fully automated DIB extraction 2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE Hot Telluric Calibrators method able to measure DIB equivalent widths (EWs) without any user interaction during the fit. The code was developed using the IGOR PRO environment2 . Note that the reduced pseudo-normalized spectra of DR12 are corrected from telluric lines contamination. Thus, no telluric correction is needed.

In the following, we give the details of our fitting technique, we explain how we extracted the EW of the DIB and how we estimated their associated uncertainties.

Profile fitting

We fit each TSS spectrum to a model made out of the product of several components as follows:

M λ = [S λ ] α × DIB[σ, λ, D] × (1 + [A] × λ) (2.2)
with:

• [S λ ] α , an adjusted stellar spectrum: S λ is the initial stellar model provided by the APOGEE project. As we mentioned above, this model is not optimized for the TSSs that are hotter than the main targets. Therefore, even if the velocity of each target star was accurately determined and the global shape of the spectrum was most of the times adequate, the depth of the stellar atmospheric lines was not properly estimated (see Figure 22). We included a scaling factor (α) in order to take this into account and to adjust the model depth to the data.

• DIB[σ, λ c , D], the DIB profile: It was modeled as a Gaussian function with three free parameters associated to its width (σ), central wavelength (λ c ) and depth (D).

• (1 + [A] × λ), a local continuum: this was a simple 1-degree polynomial introduced to model as closely as possible the continuum around the DIB.

We selected a pre-defined spectral range for the fit restricted to the vicinity of the those spectral ranges affected by artifacts due to imperfect sky emission correction or other sources of uncertainty. We use the Levenberg-Marquardt algorithm implemented in IGOR PRO to compute the coefficients that minimize the chi-square χ 2 . An example illustrating our fitting procedure is shown in Figure 22.

DIB equivalent width and error estimates

In the following, we explain how we extracted the DIB equivalent width from our fits and how we estimated the uncertainties associated to each of the parameters presented in the catalog.

• Computation of the DIB equivalent width: Since none of the previous or present observations reveals any asymmetry of the 15273 Å DIB, and for our sightlines the linear regime prevails, we assume that the actual DIB absorption has a shape that is similar to the mono-Gaussian model. We also neglect the small departures from a Gaussian that result from multiple cloud superimposition that is small compared to other sources of uncertainties. Subsequently, assuming that the continuum I 0 (λ) is reasonably fitted,

EW = DIB I 0 -I λ I 0 dλ = √ 2π D σ (2.3)
where I(λ) is the observed spectrum.

• Computation of standard deviations in residuals: We calculated the standard deviation of the fit residuals, data -model, in a region close to the DIB (region A=[λ c -10, λ c + 10] Å). In addition to this, we performed a second fit using the same function as the one presented in Eq. 2.2 but over the whole spectral range covered by APOGEE. As a matter of fact, we found that the first and third terms of Eq. 2.2 are good enough to represent continuum and Bracket lines in the entire APOGEE spectral region. For this fit the DIB term in the equation has the same restrictions as in the local fit. The DIB is fitted to ensure an optimum placement of the continuum in the DIB region and subsequently everywhere. As a matter of fact, omitting the DIB could bias the continuum in its spectral region and react on the whole fit. The residuals from this additional fit were used to obtain two additional measurements of the standard deviation. The first one was calculated over a clean (i.e. free from DIB absorption, strong stellar lines and telluric residuals) spectral range (region B= [15 892-15 959] Å). The second one (region C= [15 200-15 250] Å) corresponds to a region relatively close to the DIB (and also free of DIB) and potentially contaminated by stellar residuals. Hereafter, we will call the standard deviation R for all the three regions, R A , R B and R C . These were used for the selection criteria discussed in Section 2.4. Also, the standard deviation R A in the narrow spectral range around the DIB is used below to estimate the uncertainty on the DIB equivalent width.

• Uncertainties on the DIB central wavelength and width: The IGOR algorithm uses data and fitted model to evaluate the data-model standard deviation in the spectral range used for the fit, within the implicit assumption that the fitting function is ideally adapted to the data and the data are characterized by random Gaussian noise. If there are telluric line residuals or any other artefacts that amplify the signal fluctuations, the estimated standard deviation is correspondingly increased and treated as random noise. Based on the standard deviation and the mutual influence of the parameters the algorithm estimates one sigma errors on each parameter. We use these error estimates on both the central wavelength and the width.

• Uncertainty on the DIB equivalent width: For this quantity we estimated the error in a very conservative way in order to take into account the depar-tures from Gaussian noise and the ideal fitting function, as well as potential subsequent misplacements of the continuum level. As a matter of fact in a number of cases telluric absorption or sky emission correction residuals are present and for a number of targets the stellar line shapes are not perfectly fitted. This may result in slight displacements of the fitted continuum that do not impact the line center and impact the width in a negligible way but may impact the depth. For the most conservative estimate of the error we used the standard deviation of region A, coupled to the maximum DIB width deduced for each sightline from HI 21 cm data (see Sect. 2.4). The maximum error was estimated as the equivalent width of a Gaussian with the maximum width of the H I 21 cm line and a depth equal to R A :

err(EW) = √ 2π R A σ H I 21cm (2.4)
We checked that this conservative error is appropriate to all types of spectra and data quality, including those cases where there are clear residuals linked to stellar or telluric lines. Figure 23 shows the estimated error as a function of the equivalent width for all targets of the catalog. It can be seen from the figure that due to our method based on HI, for the same EW the error is significantly larger in directions where the ISM radial velocity range is very large, mainly here at Galactic longitudes smaller than 70 deg. This confirms that our quoted values are very conservative.

Validation criteria

Selection of fitted absorptions

We applied a series of tests to the fitted Gaussian absorptions based on the fitted parameters, the signal quality in the DIB region and elsewhere, and finally the LOS direction and the corresponding Galactic gas. The series of tests is schematically represented in Figure 24.

• First global test: the first selective test eliminates those absorptions that are deeper than 10% of the continuum (arrow (a) in Figure 24). Those depths are extremely unlikely for Galactic DIBs and especially for the TSSs that are nearby. Indeed, we checked a posteriori that all those cases correspond to spurious detections (stellar lines for the coolest or most metallic objects, unusually strong telluric residuals, etc). We also eliminated simultaneously absorptions centered outside the spectral range [15268-15280] Å. In the red, the corresponding large Doppler shifts of ≥+137km s -1 are unrealistic for the nearby ISM. In the blue, the test excludes a tiny fraction of fits with unrealistically high negative Doppler shifts caused by a stellar line (see the histogram in Figure 25).

• Test on the absorption depth: as a second test for the selected targets (the arrow (b)), we compared the depth of the fitted absorption line with the taneously to keep actual detections of narrow DIBs. A typical example of such narrow absorptions that pass successfully the test is shown in Figure 26. It can be seen from the figure that it looks convincingly like an actual DIB. The upper limit of 1.0 Å is chosen for the following reasons: for those absorptions that are very shallow (arrow (g)), this maximum value eliminates flat and elongated features of uncertain origin. Conversely, those very shallow absorptions that are reasonably wide pass the test and are falling in the upper limit category (yellow box in Figure 24. For those absorptions that are deeper and correspond to arrow (f), this maximum value will result in definitely rejecting those features that are broader than 1.5 σ H I21cm (see below).

• Limits on the width σ based on HI 21cm emission spectra: For all absorptions that exceed the noise level, either globally or locally (arrows (d) and (e)), a subsequent test on the DIB width is performed based on radio 21cm HI spectra. As a matter of fact the velocity range for some fitted absorptions 22.

is clearly not compatible with the velocity distribution in the Galaxy. Such unrealistic absorption widths are caused by an improper modeling of the stellar lines, residuals of telluric lines, or a locally increased level of noise.

In order to eliminate these spurious or biased detections from the catalog we added the following constraint on the DIB width based on the radial velocities of the atomic hydrogen gas. Indeed as the HI is the most common and widespread form of gas in the galaxy, its extent in velocity should encompass the velocity extent of the DIB, as verified in previous DIB measurements (see e.g. Puspitarini et al. 2015;Zasowski et al. 2015).

We estimated the maximal radial velocity width of the DIB by constructing a virtual, composite DIB based on the HI spectra. To do so we used the results of the HI line decomposition method presented in Planck et al. (2015). We briefly recall the principle of this decomposition.

Fraction of the article written by Quentin Remy and describing his work -The first step is to detect the significant lines in the brightness temperature T B (v) spectra of the LAB Survey [START_REF] Kalberla | The Leiden/Argentine/Bonn (LAB) Survey of Galactic HI. Final data release of the combined LDS and IAR surveys with improved stray-radiation corrections[END_REF]. We measured the dispersion in temperature, T rms , outside the bands with significant emission. In order to limit the number of false detections caused by strong noise fluctuation we clipped the data to zeros except if T B (v) > T rms in 3 channels (3 km s -1 ).

= ηL + (1 -η)G (2.5)
with 0 < η < 1 the shape factor, L a Lorentzian, and G a Gaussian respectively defined as :

L = h 1 + v-v 0 σ 2 (2.6)
and,

G = h exp v -v 0 σ 2 (2.7)
where v 0 is the position of the line, σ the width and h the height parameter.

In order to take into account the detection uncertainties, the position of the line v 0 is allowed to vary in a ± 4 km s -1 range around the initial position. For each detected line the free parameters η, h, σ, and v 0 are fitted by means of a χ 2 minimization. -End of fraction from Quentin Remy

For each LOS the computed HI decomposition is used to construct a virtual DIB, the one that would be measured in the spectrum of a star located beyond all the HI clouds, i.e. at the periphery of the Galactic HI disk. To do so, each HI component is associated to a DIB that has a width σ 0 = 1.4 Å and a radial velocity equal to the one of the HI line. The chosen width σ 0 corresponds to an upper limit on the intrinsic DIB width that can be derived from the Figure 6 of Zasowski et al. (2015). To convert the HI line into a DIB equivalent width we use the following relationships:

i) The HI column density is calculated in the optically thin limit:

N H I = 1.823 × 10 18 T b (v)dv (2.8)
where T b (v) is the 21 cm line brightness temperature at radial velocity v.

ii) The HI column density is transformed into reddening using the N H I -E(B -V) empirical relation from Gudennavar et al. (2012) and the aver- age The function representing the total virtual DIB through the HI disk is plotted in black. Note that for a better visualization, the scales for the total DIB and individual components are different.

R v = A v /E(B -V) value of =3.1: A v = 3.1 × 1.6 × 10 -22 × N H I (2.
iii) The extinction is converted into a DIB equivalent width using the relation derived by Zasowski et al. (2015):

EW DIB = 0.102 × A 1.01 v ( Å) (2.10)
The depth of each individual DIB is computed from the EW and the width.

The final virtual DIB is the product of those individual DIBs and as such it is broadened to match the velocity distribution of the HI spectrum for the entire path through the Galaxy. Figure 27 illustrates the construction of the virtual DIB for a typical LOS with 6 HI clouds. This virtual DIB is fitted to a Gaussian and the resulting width σ H I21cm is used to impose a limit on the APOGEE absorption width.

Such a limit is ensuring the compatibility between the measured DIB width and the velocity spread seen in HI. Absorptions that do not pass the test (i) as shown in Figure 24 enter test (f). The broad absorptions will be discarded

Special cases

We applied target selection criteria and considered separately Be Stars and stars from the field 4529 because they can yield difficulties in fitting. To do so we used the [START_REF] Chojnowski | High-Resolution H-Band Spectroscopy of Be Stars With SDSS-III/Apogee: I. New Be Stars, Line Identifications, and Line Profiles[END_REF] catalog of Be stars and the information contained in the APOGEE tables. All these spectra have been the subject of one by one visual inspection and only those where the DIB was clearly unambiguously and accurately detected were kept. Figure 28 contains an example of a Be star spectrum and a spectrum from a star in plate 4259.

The different possible outcomes after the selection criteria described in section 4.1 and 4.2 have been flagged from 1 to 7. Figure 28 presents one representative example for each category.

The catalog

A fraction of the catalog of all detected DIBs is presented in Tab. 22 while a summary of its statistical properties appears in Tab. 21. For the total of 6 716 detections or upper limits the catalog lists the 2MASS identification, then the equivalent width, central wavelength and DIB Gaussian width and associated errors. The flag allowing to identify the selection criteria for each target is also added. Finally, we included for completeness the estimated equivalent width for the virtual DIB as calculated from the emission in the H I 21 cm line. The various categories (1) Upper limit (2) Stars from plate 4529 (3) Be Stars (4) Narrow recovered DIBs (5) Narrow DIBs (6) Recovered (7) Detected comprise 362,11,52,122,473,572 and 5124 TSS targets respectively. Figure 29 shows the location of all TSSs on a sky coordinates plot, distinguishing those that are included in the catalog. It can be seen that at Galactic latitudes smaller than ≃ 15 degrees all APOGEE fields contain TSSs with detected DIBs, while at higher latitude some fields have no targets included in this catalog, due to the weakness of the IS column.

The comparison between the velocity distributions of the original sample Figure 29 Location of all TSS targets on a sky coordinates plot, distinguishing those that are included in the catalog (colored circles, color representative of the EW) from those not included (black circles).

and the one included in this catalog is presented in Figure 25. Both of them peak at ∼ -20 km s -1 . However, while the final distribution resembles to a Gaussian distribution, the original one is quite irregular, with several secondary peaks. In particular those at ∼ -260 and -130 km s -1 correspond to very strong stellar absorption features that have been successfully eliminated. Note that the final sample presents a relatively small (i.e ∼ < 30) number of detections with high velocities (i.e. ∼ 100 km s -1 ). After visual inspection of the corresponding spectra, we did not see any evidence for a spurious detection. Therefore we kept those detections in the catalog. They will be the subject of further work. We cannot exclude a similar high velocity tail in the blue due to the threshold imposed to remove spurious identifications due to stellar lines. Figure 210 displays the distribution of the DIB widths. Only a very small fraction of selected DIBs have sigma above 3 Å. In the same way, Figure 211 shows that there are very few DIBs (∼167) with equivalent width > 200 m Å. This is not unexpected since TSS are relatively nearby stars, much closer than the APOGEE targets full sample.

Finally, we show in Figure 212 a comparison between the APOGEE measured EWs and the EWs of the virtual DIBs described in section 2.4 that are proportional to the total hydrogen column from the LAB Survey [START_REF] Kalberla | The Leiden/Argentine/Bonn (LAB) Survey of Galactic HI. Final data release of the combined LDS and IAR surveys with improved stray-radiation corrections[END_REF], converted into an equivalent width according to equation 2.4. The figure shows that, within the error bars, there is an envelope to all the data points that corresponds roughly to a one-to-one relation. This supports the robustness of our method and the choice of the conversion factor. Data points close to the envelope very likely correspond to target stars located beyond all the H i clouds seen in emission, while points well below the envelope correspond to targets that are in front of at least one cloud.

Summary

We have analyzed the series of SDSS/APOGEE NIR spectra of the calibration stars used to decontaminate data from telluric absorption lines. Those targets are generally blue stars and the APOGEE Stellar Parameters and Chemical Abundances Pipeline (ASPCAP, García [START_REF] Pérez | ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline[END_REF]) stellar models cannot be used straight away. We have used them as a starting point and allowed the model broad stellar features to vary in depth to reproduce the observed continuum. In the region of the 1.5273 µm DIB this adjustment has proven to be good enough to allow the extraction of the DIB equivalent width and central radial velocity through Gaussian fitting of the DIB absorp-50 2.6. Summary Note. -Table 22 is published in its entirety in the electronic edition of the Astrophysical Journal. A portion is shown here for guidance regarding its form and content.

2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE Hot Telluric Calibrators tion. Careful and severe examinations of the DIB parameters, the continuum shape and the quality of the adjustment were done. This result in a conservative selection of reliable DIB parameters. In particular all DIB candidates that contradict radial velocity limiting values based on the HI 21 cm spectra have been excluded. A total of 6 716 lines of sight are selected and the results are presented in a catalog available from CDS, Strasbourg . The statistical properties of the resulting DIB database show that our selection criteria are fully appropriate.

Since most of the calibration stars are nearby objects (say, within the first kpc), such a DIB catalog can be used to improve 3D maps of the nearby ISM and to complement maps at larger scale as we will see in Chapter 4.

Chapter 3

A catalog of 1.5273 µm Diffuse Interstellar Bands based on late-type stars

The latest SDSS/APOGEE data release DR14 (August 2017) has provided an increased number of stellar spectra in the H band and associated stellar models using an innovative algorithm known as The Cannon. We took advantage of these novelties to extract as much as possible measurements of the 15 273 Å near-infrared DIB from the red giants stars. We modified our automated fitting methods dedicated for the TSSs and used in Chapter 2 with some adaptations motivated by the change from early-or intermediatetype stars to red giants. A new method has also been developed to quantify the upper limits on DIB strenghts. Careful and thorough examinations of the DIB parameters, the continuum shape and the quality of the adjustment of the model to the data were done. Our analysis has led to the production of a new catalogue containing 124 064 new DIB measurements.

Introduction

The infrared high resolution (R ∼ It is obviously more difficult to extract the DIB from the late-type stars since the interstellar features are blended with deep stellar lines. This explains why most of our reliable DIB-fitting results of Chapter 2 were obtained for hotter stars. However, if the stellar models well reproduce the data, the extraction can be performed without ambiguities due to stellar blends. This is the case of the latest APOGEE data release DR14 which includes the Cannon synthetic spectra. In this chapter, instead of using TSSs, our sample is made up of the science targets, mainly red giants stars.

Cannon model spectra (new in DR14)

Highly detailed model spectra were fitted to the APOGEE data and made publicly available for a large number of targets. These models greatly facilitate the extraction of the diffuse interstellar bands present in the APOGEE spectral intervals. While the DR12 synthetic spectra used the APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP; García Pérez et al. 2016a) for the derivation of the stellar atmospheric parameters and abundances, in DR14 a new and important change is the introduction of an innovative data-driven machine-learning technique known as "The Cannon" [START_REF] Ness | The Cannon: A data-driven approach to Stellar Label Determination[END_REF]. This algorithm parameterized the spectral fluxes as a function of a set of independently-determined stellar parameters and abundances. For DR14, Cannon-2 code by [START_REF] Casey | The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses[END_REF] was trained on ASPCAP products for a subset of high S/N giant stars, and the model was applied to all objects within the range of parameters covered by the training set with some modifications listed in Abolfathi et al. (2018).

Here we analyze all available red giant stars (160 000) from the public Cannon data products. To do so we used the CannonStar file which contain Cannon labels, normalized observed spectra and associated uncertainties as well as synthetic spectra for each single star. Such Cannon results can be found at path1 /cannon/LOCATION ID/cannonStar*.fits. The difference between

DIB extraction

Cannon and ASPCAP has significant consequences on our DIB extraction, as will be detailed in the next section.

DIB extraction

M λ = [S λ ] α × DIB[σ, λ, D] × (1 + [A] × λ) (3.1)
where each DIB is modeled as a Gaussian function with three free parameters associated to its Gaussian RMS width (σ), central wavelength (λ c ) and depth (D) as follows: During the fit itself the adjusted Gaussian curve representing the DIB is rejected or selected depending on its depth D, its width σ and spectrum quality criteria derived from the preliminary tests. However, here some fitting constraints have been modified to take into account the differences between the TSSs and the science targets:

DIB[σ, λ, D] = 1 + D × e -(λ-λc
• Stellar model adjustment: the Cannon stellar models are optimized for red giant stars, and they perfectly reproduce the observed data. It was not the case of the TSSs that are bluer stars for which it was necessary to adjust the stellar models. Here we simplified our constraints on the adjustment of the stellar synthetic spectra. We still allow for a global increase or decrease of all stellar lines, however the prior value for the adjustment coefficient α is always unity. The fitted coefficient is always very close to one and the continuum is also always very close to a straight line of value 1.

• DIB depth upper limit: TSSs are nearby stars whereas the APOGEE main targets can be very distant and DIBs can be much stronger. For this reason no upper limit was applied to the DIB depth.

• DIB depth lower limit: the stellar model is so close to the data in all spectral regions that the standard deviation R A of the data-model residuals in the region close to the DIB (region A) is a good enough estimate of the measurement error on the DIB depth. As a consequence the new acceptance criterion is no longer based on a comparison between the DIB depth and the standard deviations of residuals in regions A, B, and C, but is now solely: Depth D ≥ R A .

• DIB radial velocity range: TSSs being nearby stars, the absorbing interstellar matter for those targets is characterized by a narrow radial velocity range. This is no longer the case here for the distant giant stars. As a consequence we widened the allowed interval on radial velocities with respect to the one used for TSSs. Specifically, we now restricted the spectral range for the DIB center to [15 260-15 286] Å.

• DIB width upper limit: we no longer used HI 21cm data to limit the total width of the DIB, instead we used limits based on the FWHM histograms from our study based on TSSs and Zasowski et al. (2015). The latter correspond to a broad range of star distances, broad enough to be valid here.

• DIB width lower limit: we kept the same lower limit of 0.7 Å on the DIB width, supposed to be close to the intrinsic width as determined by Zasowski et al. (2015) and in Chapter 2 and with some allowance for noise-related uncertainty.

Fig. 31 shows some examples of our fitting method.

3.3.2 DIB upper limit estimate in case of no convergence or no DIB detection

If the adjustment described previously failed by absence of convergence (we call it the non-convergence case) or there was convergence but the fitted absorption is found to be smaller than the noise level (we call it the upper-limit case, although, as we will see some spectra, at the final stage of analysis, this term is not appropriate), then we re-initiated a second fitting procedure with the goal of extracting equivalent widths or upper limits in a more efficient way with more appropriate initial conditions. The total number of targets falling in the two categories and re-analyzed is 45 393. First, we fitted the combination of a continuum and a depth-variable stellar model to the data (i.e, removing a DIB contribution from the global model), this time masking the DIB area. In case of convergence, this ensures that the fitted continuum and the synthetic spectrum reproduce well the data outside the DIB.

• Case 1: If the fit fails again -this is the case when the model is not adapted to the data or if there is a problem of continuum, a new standard deviation on residuals will be calculated based on the difference between the data and the initial model provided by the APOGEE pipeline.

• Case 2: If the fit converges then the new residuals (difference between the data and the no-DIB adjusted model) are used for an updated estimate of the standard deviation.

The second step consists in fitting a simple Gaussian to the data-model residuals in the region of the DIB. To do so, we use the same initial guesses and constraints used for the automated fitting method as described in Subsect. 3.3.1.

• If the Gaussian fit does not converge, either because it has not found a solution or because there is a real absence of detectable DIB, then a linear adjustment is performed i.e, on the continuum over the wavelength range chosen for the DIB EW [15 260-15 286] Å. The selected value for the EW is then zero and an upper limit on EW is estimated from the standard deviation of the residuals around the linear fit Std dev and the average value of the width of the 15 273 Å DIB taken as 1.7 Å:

EW = 0 ± 1.7 × √ 2π × Std dev (3.3)
Some representative examples are shown in Fig. 32.

• If the fit finds a solution, then the coefficients of the fitted Gaussian (σ gauss ; D gauss ) will be used either as DIB parameters in case 1 or as initial values for a new automated fit following Eq. 2.2 in case 2. Equivalent widths in both cases will be derived following Eq. 2.3 as EW= √ 2π D σ where the widths are σ = {σ gauss ; σ f it } and the depths are D={D gauss ;D f it } respectively. The errors are extracted as follows:

Error DIB = 2 √ π × σ × Error depth + 2 √ π × σ × Std dev (3.4)
These cases are illustrated in Fig. 33. Finally, for better understanding of our algorithm, Fig. 34 shows a graphical representation that maps out our stepby-step process to determine the upper limits. It is interesting to note that this additional treatment is quite productive: for ∼ 2/3 of cases, the change of initial guesses for the DIB in the fitting procedure allows the Levenberg-Marquardt algorithm to converge to a good solution and a χ 2 significantly smaller than for the first fit. This can be explained by the existence of large Doppler shifts between the star and the absorbing ISM. As a matter of fact, in our first adjustment the initial condition for the DIB center was a null radial velocity of the absorbing medium in the stellar frame. In case of large differences between the star radial velocity and the absorbing ISM radial velocity, the initial position of the DIB center was accordingly distant from the actual DIB center, and in some cases the adjustment could subsequently stop at a secondary χ 2 minimum. This no longer happens in the second adjustment with an initial guess for the DIB center now very close to the best fit at the primary χ 2 minimum. Similarly, peculiar or poorly modeled stellar features could favor a secondary χ 2 minimum during the first adjustment, when they were combined with an initial position of the DIB far from the actual one. Using instead an initial guess much closer to the solution leads to the optimal solution.

The catalog

The extraction procedure allowed to extract EW measurements of the 15 273 Å DIB for 124 064 different sight-lines. The catalog of equivalent widths and associated uncertainties, radial velocities and widths will be made available at the Strasbourg Data Center (CDS) after merging with the updated catalog of DIBs in the TSS spectra of Data Release 14, when available.

Note that, because the DIB is a weak absorption, despite the high quality of the spectra and stellar models, measurement uncertainties on the EW become on the order of the EW itself for EW 0.02-0.05 Å. This corresponds to A V 0.5-0.8 mag according to the average relationship (see below). This will result in some limitations in our analyses and correlative studies.

Conclusion

We have analyzed red giants NIR spectra from the last SDSS/APOGEE survey data release (DR14) with the goal of extracting the 15 273 Å DIB. To do so, we used the code developed in Chapter 2 with some adaptations motivated by the change of target types, namely from early-or intermediate-type stars to red giants. The use of the stellar models computed by means of the datadriven "Cannon" algorithm greatly facilitates the analysis. In the region of the 15 273 Å DIB the adjustment has proven to be good enough to allow the extraction of the DIB equivalent width, width and Doppler velocity through Gaussian fitting of the IS absorption. This resulted in a conservative selection of reliable DIB parameters for a total of 124 064 lines of sight. In the following, we illustrate the potential of these catalogs by means of two examples: DIB as tool for ISM tomography and the study of the conditions of appearance/disappearance of DIBs. These applications will demonstrate the potential interest of such catalogs and the amount of data and analysis one can get from the sightlines surveys.

Part II

Potential applications of DIB catalogs

Chapter 4

DIBs as tracers of the nearby ISM

Introduction: DIBs as proxies for column densities of IS matter

DIBs are clearly correlated with interstellar matter and they have been used to locate spiral arms and build maps of reddening radial profiles (Kos et al. 2014;Puspitarini et al. 2015). The 15 273 Å DIB is correlated with the extinction over at least three orders of magnitude (Zasowski et al. 2015). Thus, it can be used as an independent approach to estimate it or as an initial guess in absence of better information. Note that we will demonstrate in the next chapter the existence of a break of the 15 273 Å DIB proportionality to the extinction at high extinction levels (a saturation related to the so-called "skin effect" already observed for a few DIBs), however this does not prevent the use of DIBs for cloud distance assignments.

We crossmatched the TSS catalog presented in Chapter 2 with the compilation of reddening measurements provided by Lallement et al. (2014) and used to build local 3D maps of the ISM. We have made several tests of DIBbased IS mapping in 3D, starting with very simple constraints on cloud minimum distances up to actual 3D inversion of DIB equivalent widths. Figure 41 shows the comparison between the APOGEE 1.5273 µm DIB equivalent width and the reddening for the 221 stars in common. There is a clear positive correlation showing that the fitted absorptions contain valuable information on the intervening ISM.

The figure also presents the linear fit derived by Zasowski et al. (2015) for the APOGEE cool stars.

Our data points cluster around this relationship in solid agreement with the results obtained for the late-type stars. We note however that the number of outliers with very weak DIBs is significantly higher than the number of outliers with strong DIBs. A possible explanation of this trend is the influ- (2015).

ence of the radiation field of the early-type stars that are part of the targets. This effect has been noticed and documented for many years (see e.g. Vos et al. 2011, for a very convincing large study). Indeed, departures from a linear relationship are much weaker when using cooler stars as targets, as shown by Raimond et al. (2012). This effect can be seen as a disappearance of the DIB carrier far outside the cloud under the effect of strong UV fields of unshielded areas, however as we will see later it can also be seen as the disappearance of the DIB carrier deep within the cloud, on the other side of the "skin". We will come back to this point in the discussion.

Likewise, the catalog can be crossmatched with other DIB data from available optical spectra. Being relatively bright many of them have been the subject of specific investigations, allowing comparisons with other DIBs and atomic or molecular lines. We have found at least 100 targets that have been observed at very high resolution and are available from public archives.

Finally, as mentioned in the Chapter 2, one of our main interests is the ISM 3D tomography. Compared with the extended catalog of Zasowski et al. (2015) that allows mapping the Galactic scales, the TSSs are more appropriate for the nearby ISM. As a proof-of-concept we explored this use in the Taurus-Perseus region. This is illustrated in Figure 42 that shows the dust optical thickness measured by Planck in this area (Planck Collaboration et al.

Introduction: DIBs as proxies for column densities of IS matter

Figure [START_REF] Hartmann | Investigations on the spectrum and orbit of delta Orionis[END_REF] Planck dust optical depth in the Taurus-Perseus region with superimposed APOGEE TSS targets selected for the catalog. Blue (resp. red) markers correspond to a DIB equivalent width lower (resp. higher) than a threshold of 50 m Å. In regions devoid of dust (white or pale grey) DIBS are uniformly below the threshold. Towards dense clouds there is a mixed composition that reflects the location of the target (see Figure 43). 2014). Superimposed on the map are the TSS targets color-coded according to the detection (or non-detection) of a DIB with an equivalent width higher than 50 m Å. The strength of the DIB is always small in regions with negligible or small dust emission while the contrary is seen towards opaque clouds. Note that the "skin effect" we mentioned and that will be studied in Chapter 5 is not apparent here in our 'YES' or 'NO' DIB simplified criterion.

We selected two regions, marked as A and B in Fig. 42, to explore the use of this catalog to locate the molecular clouds along the line of sight. For that, we used the subset of targets in these areas with Hipparcos parallax [START_REF] Perryman | The HIPPARCOS Catalogue[END_REF][START_REF] Van Leeuwen | Validation of the new Hipparcos reduction[END_REF]. Figure 43 shows a comparison between the detection (or absence) of a DIB towards a given target and the distance to it. For both cases there is a clear cut-off in distance between this two categories. This allows to bracket the distance to the main absorbing cloud: targets without detection are in front of the clouds while those with detection are well beyond.

For our particular cases the main clouds are at about 150 pc, in agreement with the detailed tomography of Schlafly et al. (2014). Note that we did not update this study of Taurus with TSSs and Gaia DR2 parallaxes yet.

The reason is that in order to reach a good precision in cloud distances we need a large number of constraining targets and to add cooler stars.

As a consequence, we have planned to preliminary merge with the catalog of DIBs from APOGEE science targets (catalog in progress as explained in Chapter 3).

Contribution to 3D ISM mapping

Multi-wavelength 2D emission maps of the Galactic interstellar gas and dust are extremely detailed but lack distance information. The production of 3D maps, future tools of wide use, is in progress (e.g., Schlafly et al. 2014;Green et al. 2015). Most are dust maps based on stellar photometric and spectroscopic surveys that provide distance-limited extinction/reddening measurements. As tracers, DIBs (and gas absorption lines) are more powerful than extinction/reddening since they provide valuable information on the lineof-sight cloud structure and the radial velocities of the clouds (kinematics).

From the point of view of the Galactic evolution, a better understanding of the interstellar matter cycle requires a deeper knowledge on how the kinematics of the ISM is linked to the properties of the stellar population. Moreover, velocities also allow to link absorption and emission data that possess a spectral information.

As discussed in Part. I we have developed the appropriate spectral analysis tools based on state-of-the-art synthetic spectra to make use of stars of all types (early-type stars and the more numerous late-type stars). Data on 4.2. Contribution to 3D ISM mapping some important nearby cloud complexes are missing from stellar surveys (because they are not the focus) or due to biases towards bright target stars in previous observing programs. Our catalogs from high resolution nearinfrared spectra make it possible to probe with accuracy those dense clouds.

Capitanio et al. 2017 illustrated the potential of these catalogs to build detailed 3D maps of the ISM. In this work, we updated and tested several refinements. My contribution was to complement this database with a series of ∼ 5000 color excess values estimated from the strength of the λ15273 DIB toward stars possessing a Gaia parallax. To do so, the series of 25 409 DR12 spectra with Tycho identifier were analyzed using the methodology presented in Part. I as a starting point. All details of my contribution are described in the subsections 2.2.1. to 2.2.3 of Capitanio et al. ( 2017) presented below. The ultimate goal is extracting the maximum amount of DIBs from available datasets and contributing to the production of Galactic 3D ISM maps as precise as possible, starting with the Local ISM and extending gradually to larger distances following the previous team works (Lallement et al. 2014). Gaia parallaxes recently published contribute to achieve high spatial resolution and accuracy (Gaia Collaboration et al. 2016). In addition, the composite data (DIB+extinction) corresponding to ∼ 30,000 targets using by Capitanio et al. ( 2017) were merged recently with new extinction measurements from APOGEE-DR14 in Lallement et al. 2018a. The authors mapped the Local Arm using a Bayesian inversion of absorption data with the more accurate and numerous parallaxes provided by Gaia.

Introduction

Three-dimensional (3D) maps of the nearby and distant Milky Way interstellar medium (ISM) are useful multipurpose tools have been only recently developed at variance with the extremely detailed, multiwavelength 2D emission maps or spectral cubes that have been accumulated over decades. As a matter of fact, a revolution is underway that dramatically changes the situation: photometric, spectrophotometric, and spectroscopic massive stellar surveys have started to provide extinction and absorption data that, coupled with parallax or photometric star distances, bring the third dimension for the ISM, i.e., the distance to the clouds. Thanks to 3D maps of the interstellar matter, structures overlapping on the sky are now converted into distinct clouds distributed at different distances, which will progressively allow emission components to be assigned a well-located production site. Three-dimensional maps or pseudo-3D maps have already been produced based on various kinds of stellar data; that is mainly photometric extinction but also diffuse interstellar bands ⋆ Our online tool is available at http://stilism.obspm.fr (DIBs) and spectral lines of gaseous species (Marshall et al. 2006;Vergely et al. 2010;van Loon et al. 2013;Welsh et al. 2010;Lallement et al. 2014;Schlafly et al. 2014;Sale & Magorrian 2014;Kos et al. 2014;Schultheis et al. 2014;Zasowski et al. 2015;Green et al. 2015;Schlafly et al. 2015;Rezaei Kh. et al. 2017). Various methods have been used to synthesize the distance-limited data, including full 3D tomographic inversion of individual sightline extinctions (Vergely et al. 2001;Sale & Magorrian 2014), statistical methods based on photometry, and stellar population synthesis or color-color diagrams; see the Monreal-Ibero & Lallement (2015) proceedings for recent developments in this field. In the coming years, maps of increasing quality are expected thanks to continuing or new stellar surveys, on the one hand, and precise Gaia parallaxes, on the other hand.

Each mapping technique has its advantages and limitations. Statistical methods require large amounts of targets and are appropriate at large distances, while the synthesis of individual line-of-sight data may work closer to the Sun. Near-infrared (NIR) or infrared (IR) surveys allow one to go deeper in cloud opacities compared to optical data but are more difficult to correct for telluric emissions and absorptions. Maps based on Article published by EDP Sciences A65, page 1 of 17

A&A 606, A65 (2017)
DIBs may slightly suffer from the fact that DIB carriers tend to disappear in dense cores of clouds (Lan et al. 2015), thereby potentially reducing the resolution. Nevertheless, there are numerous DIBs and their combinations may become powerful tools to probe the physical state of the encountered media. Whatever the technique, a major difficulty is associated with the decrease with distance of the achievable spatial resolution in the radial direction, due to increasing uncertainties on target distances. Full 3D inversions that combine the radial and ortho-radial information may partly overcome this difficulty provided they are based on massive datasets, however at the price of strongly increased computing time (see, e.g., Sale & Magorrian 2014).

We present a first attempt to address these limitations by means of a combination of three different datasets as follows: (i) individual color excess measurements from broad-and narrowband photometry of nearby stars (mostly ≤500 pc); (ii) NIR DIB equivalent widths (EWs) measured in individual spectra of more distant stars (500-1500 pc); and (iii) color excess measurements at larger distance (up to 3 kpc) based on a statistical analysis of multiband photometric data. Our purpose is to test such an association and its potential for improved 3D mapping of nearby or distant ISM. The first two datasets are merged by means of a conversion from DIB strengths to E(B -V) color excesses. A Bayesian inversion is applied to the resulting composite database. The large distance color excess data are used to build a very low-resolution 3D distribution that is subsequently used as the prior distribution in the Bayesian inversion to replace a plane-parallel homogeneous distribution.

Prior to the combination of these different sources, we analyzed the changes that are induced in the inverted 3D maps based on the first color excess dataset alone when Hipparcos or photometric distances are replaced with new Gaia Data Release 1 (DR1) parallax distances (the TGAS catalog), when available.

In Sect. 2 we describe the three datasets used in the inversions. In Sect. 3 we briefly recall the Bayesian technique developed in Vergely et al. (2010) and Lallement et al. (2014; hereafter LVV) and the changes introduced in the method. We also describe the hierarchical method developed to build the large-scale prior distribution. Section 4 describes the 3D map evolution introduced successively using Gaia distances, the addition of the DIB-based data, and finally the large-scale prior map. Section 5 discusses uncertainties and shows comparisons between the resulting final map and other measurements of cloud distances or cavities. Section 6 discusses the results and perspectives of such composite maps.

Data

Individual color excess data

We started with the reddening dataset of ≃22 500 sightlines compiled and inverted by LVV. It comprises four different catalogs that were cross-matched and homogenized; for more details about the stellar types and characteristics of the calibration method for each separate dataset, see Vergely et al. (2010), Cramer (1999), Nordström et al. (2004), Dias et al. (2012). Most target stars are nearby (d ≤ 500 pc), cluster data only correspond to larger distances. The four catalogs are complementary in terms of sky coverage. Of the target distances used for the inversion presented in LVV, 75% were photometric. For onequarter of these distances the Hipparcos distance was preferred. In the four inversions presented here we kept the same error bars on the reddening as those used by LVV. The only difference in the LVV series of targets is the removal of ≃400 targets with photometric distances that had no catalog identifications. The coordinates, brightnesses, estimated distances, and reddening reasonably of these ≃400 targets correspond to those of nearby stars. Therefore, these targets were not excluded for the inversion presented in LVV. However, as we discuss in section 4, after the addition of new, more distant APOGEE targets and the subsequent mapping improvement we realized that for these particular targets the color excesses were significantly above other data in the same area. For this reason, we conservatively excluded the targets. Figure 1 shows the distribution of the remaining targets over the sky.

As shown in LVV, the distribution of the targets is highly biased toward low reddening regions in the sky. This had strong consequences on the resulting maps; cavities surrounding the Sun were well defined along with the boundaries of the first encountered dense clouds. On the other hand, clouds located behind foreground dense structures were either not mapped or were very poorly mapped.

Measurements of DIBs and DIB-based color excess estimates

Except for the recent identification of ionized fullerenes C + 60 , the carriers of the DIBs are still unknown, and it may seem strange to use absorptions due to unknown species for mapping purposes. However the link between DIB strengths and the columns of interstellar matter is strong and successful DIB-based 3D mapping has now been carried out (see Sect. 1). Up to now such mapping used solely DIBs. At variance with these works, here we attempted to merge color excess data and DIB absorption data and perform a unique inversion of a composite dataset. This requires a conversion of DIB equivalent widths into reddening (or the inverse); because none of the DIBs have been shown to be perfectly correlated with the reddening, such a mixing of color excesses computed in so many different ways -for example, with photometric and spectrometric techniques -is potentially hazardous. On the other hand, initial comparisons between DIB equivalent widths and reddening radial profiles in the same sightlines have demonstrated that their similarity (Puspitarini et al. 2015) and global maps also have strong similarities (Kos et al. 2014). Additionally, it is possible to account for the variable E(B -V)/DIB ratio by increasing error bars on the estimated E(B -V), which we have carried out in this work.

Data

We used the results of the SDSS-III Apache Point Observatory Galactic Evolution Experiment (APOGEE), which is one of the four Sloan Digital Sky Survey III (SDSS-III; Eisenstein et al. 2011;[START_REF] Aihara | [END_REF] experiments. The APOGEE uses a 300-fiber multiobject spectrograph working in the near-infrared (H-band; 1.51-1.70 µm) at high spectral resolution (R ∼ 22 500; Wilson et al. 2010). Specifically, we downloaded spectra from the SDSS data release 1 (DR12; Alam et al. 2015), which provides all the data taken between April 2011 and July 2014.

In addition to the ∼160 000 scientific targets selected from the source catalog of the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) and distributed across all Galactic environments, APOGEE contains a sample of ∼17 000 spectra of hot stars, called telluric standard stars (TSSs). The TSSs form the basis for the correction of the telluric absorption lines across all field types (see Zasowski et al. 2013, for a detailed description of the different target classes). These stars are the bluest stars on a given APOGEE plate with a magnitude in the range 5.5 ≤ H ≤ 11 mag and are therefore hot and bright stars with spectra that are most often featureless.

The APOGEE products contain the decontaminated spectra and synthetic stellar spectra that provide the main stellar line locations and relative depths and widths. Both were used by Elyajouri et al. (2016) to extract a catalog of λ15273 DIB measurements from 6700 TSS spectra. Further details on the sample can be found in Elyajouri et al. (2016, and references therein). Weaker DIBs have been further studied based on the same TSS spectra (Elyajouri et al. 2017). Here we focus on the strongest 15 273 Å DIB with the aim of extracting as many accurate measurements as possible from both scientific targets and TSSs possessing a Gaia parallax.

We analyzed the series of 25 409 DR12 spectra of targets belonging to the Tycho catalog to determine the presence or absence of a detectable DIB at 15 273 Å and measure its equivalent width (resp. an upper limit) in case of positive (resp. negative) detection. We took as a starting point the methodology presented by Elyajouri et al. (2016) for TSSs spectra. We kept the same fully automated fitting technique and method of classification but we adapted the fitting constraints to take into account the inclusion of targets cooler than the TSSs. The code was developed using the IGOR PRO environment 2.

Fitting method

We restricted the fit to the spectral range [15 260-15 280] Å in the vicinity of the DIB. This range is wide enough to ensure an adequate sampling of the neighboring Bracket 19-4 stellar line at 15 264.7 Å. We used for the fit the Levenberg-Marquardt algorithm implemented in IGOR PRO 2 .

We fit the data to a combination of an adjusted synthetic spectrum, DIB model, and smooth continuum as shown in Eq. ( 1). Below we provide information specific to each of the equation variables as follows:

M λ = [S λ ] α × DIB[σ, λ, D] × (1 + [A] × λ), (1)
• [S λ ] α , an adjusted stellar spectrum, where S λ is the stellar model provided by APOGEE. We included the scaling factor α to adjust the model stellar line depths to the data. • DIB[σ, λ c , D], the DIB shape was modeled as a Gaussian function allowed to vary in strength, depth, and velocity shift with three free parameters associated with its Gaussian RMS width (σ), central wavelength (λ c ), and depth (D).

• (1 + [A] × λ), a 1-degree polynomial function introduced to
reproduce the continuum around the DIB as close possible.

An example illustrating our fitting procedure is shown in Fig. 2.

Selection criteria and error estimates

Residuals, computed as differences between data and model, were used to obtain three measurements of the standard deviations RA, RB, and RC. To do so, we performed the local fit and a second fit over the whole spectral range covered by APOGEE using the same fit function as that presented in Eq. ( 1). We selected the standard deviations RA, RB, and RC of the fit residuals, as follows: These are used for the selection criteria shown in Table 1 and also used below to estimate the uncertainty on the DIB equivalent width. The uncertainty is conservatively estimated based on the following formula:

δ EW = √ 2π σ max(RA, RB, RC), ( 2
)
where σ is the Gaussian rms width that results from the fit. The quantity max (RA, RB, RC) is the maximum of the three standard deviations.

Table 1 shows the fitting constraints that were used to select our final sample. They are somewhat different from the criteria of Elyajouri et al. (2016). In particular, we no longer used HI 21 cm data to limit the width of the DIB, but we used limits based on the FWHM histograms built in this previous work. Representative examples illustrating our fitting procedure and the various categories are shown in Fig. 2.

Final data selection for the inversion

Equivalent widths and stellar rest-frame wavelengths are determined from the best-fit parameters in each sightline for the 25 000 attempted spectra of targets belonging to the Tycho A65, page 4 of 17 (420) [15 268-15 280] [1-1.4[ ]2 × RA -1.5× max(RA, RB, RC)] Upper limit ( 1753 catalog. We rejected those cases where the fit failed due to low S/N, an inadequate stellar model, or very strong telluric contamination most of the time, and we retained 9913 spectra following the criteria in Table 1. The corresponding targets were crossmatched with the targets of the Gaia DR1 catalog (Arenou et al. 2017) to obtain a catalog of 7800 stars with Gaia distances. We added to the DIB measurement an additional 50% uncertainty to account for the E(B -V)/EW variability associated with the influence of the physical properties of the encountered media. As a matter of fact, from the most neutral to the most ionized media, this ratio may reach a variability factor of three (see, e.g., Elyajouri et al. 2017). After the additional filtering for our criteria on target distances and distance uncertainties (see Sect. 4.1), only 4886 targets were retained for the present inversion. Other targets will become useful after the next Gaia data releases. The number of retained targets is small with respect to the initial number of APOGEE spectra of Tycho stars (less than 20%) and targets from the reddening catalog. Despite the limited number of these retained objects, they are precious since they allow us to probe more reddened regions, in particular to reveal a second or third rank of clouds, something often precluded using the bright targets measurements in the optical. We converted the DIB EW into a color excess using the Zasowski et al. (2015) formula

EW = 0.102 A 1.01 V Å (3)
and the classical average total-to-selective ratio A V /E(B -V) = 3.1 (Savage & Mathis 1979). The relationship established by Zasowski et al. (2015) has been confirmed, by Elyajouri et al. (2016), to be valid locally based on the closer TSSs.

Subsample of Pan-STARRS color excess measurements

Green et al. ( 2015) have used Pan-STARRS and 2MASS photometric measurements toward 800 millions targets to build remarkable 3D dust reddening maps with a very high angular resolution (5-15 arcmin) covering distances from ≃300 pc to 4-5 kpc. The publicly available maps contain a quality flag that allows to exclude results at small or large distances that are uncertain because of limitations on the number of targets for geometrical or sensitivity reasons. Both surveys were conducted from the Northern Hemisphere. We used those data to build a low-resolution 3D prior distribution to be used in our Bayesian inversion in replacement of the previous analytical solution corresponding to a homogeneous plane-parallel opacity.

To do this, we downloaded a grid of Pan-STARRS E(B -V) estimates for lines of sight equally spaced every 0.5 degree in Galactic latitude and longitude and for all distances available from the Pan-STARRS online tool, in this way obtaining 3 071 230 pairs of distance and extinction after exclusion of all data points flagged as uncertain. As result of this process, we do not benefit from the very high angular resolution of the maps, however this information would be lost in our approach. In many directions the first 200 pc are flagged because of the limited number of targets available to define the reddening in the very small solid angle chosen for this Green et al. (2015) study. However this is where the present inversion of individual data may be advantageous. The method applied to these Pan-STARRS results is described in Sect. 3.2.

Inversion techniques

Bayesian inversion of individual sightlines

The Bayesian inversion technique is based on the pioneering work of Tarantola & Valette (1982) and its first developments and applications to the ISM are described in detail in Vergely et al. (2001Vergely et al. ( , 2010)), Welsh et al. (2010), Lallement et al. (2014). Briefly, the inversion optimizes an analytical solution for the 3D opacity distribution through the adjustment of all integrated opacities along the target sightlines to the observed color excess data. Because the solution is strongly under-constrained, it is regularized based on the assumption that the 3D solution is smooth. Opacities ψ at two points (x), (x ′ ) in space follow correlation kernels: here

ψ 1 (x, x ′ ) = exp( -x-x ′ 2 2ξ 2 1 ) and ψ 2 (x, x ′ ) = 1 cosh(-x-x ′ /ξ 2 )
, where the first (resp. second) term represents the compact (resp. diffuse) structures. The correlation distances ξ 1 and ξ 2 , which roughly correspond to the minimum size of the computed structures, are evidently limited by the average distance between the target stars. Here we have chosen ξ 1 = 15 pc and ξ 2 = 30 pc. As we discuss in the next sections, such kernels are valid in internal regions where the target distribution is the densest. The model variance, which controls the departures from the prior distribution, is σ 1 = 0.8 for the first kernel and σ 2 = 1.0 for the second (see LVV for its definition). The Tarantola & Valette (1982) formalism allows one to compute the least-squares solution iteratively. For the present maps the convergence is reached in about six iterations. Uncertainties on the reddening and target distance are treated in a combined way (see Vergely et al. 2001). The distribution is computed in a volume of 4 kpc by 4 kpc along the Galactic plane and 600 pc along the perpendicular direction.

Compared to the method used in Lallement et al. (2014), an additional treatment of outliers was introduced. During each iteration except the first two, in case one measured color excess value is found to be incompatible with the kernels and the surrounding data, its error bar is multiplied by 2. This method has proven to solve for data points that have underestimated errors and eliminates oscillations. The Bayesian approach implemented in Vergely et al. (2001) and subsequent works allows us to include an a priori knowledge of the ISM 3D distribution. This prior distribution can in principle be provided by a model that represents the density variations in the Galactic plane by an analytic law or alternatively by a distribution based on external data. The latter case is fully appropriate to our objective of combining different sources of extinction. As a matter of fact, it is possible to produce density maps by successive refinements following integration of data from different sources in a sequential and non-simultaneous manner. The advantage of such an approach is twofold: first, the amount of data to be integrated at each step is reduced, which makes it possible to reduce computation times and, second, it is easier to see the contribution of each set of data.

In this perspective, a new development was introduced in our inversion code. Now, it is possible to use any 3D prior distribution, i.e., we can replace the homogeneous plane-parallel prior that corresponded to an analytical representation (we used an exponential decrease from the Galactic plane) with any arbitrary precomputed distribution. We used this possibility to test a twostep inversion based on two different datasets as described in Sect. 4.3. The construction of the prior distribution is described in the next section.

Large-scale prior distribution based on Pan-STARRS

We used the Green et al. (2015) color excess subsample described in 2.3 to develop a low-resolution opacity model extended to large distances, to be used as a prior for the inversion of individual lines of sight as described above. Because this reddening model is based on photometric measurements of hundreds of millions of objects, the Green et al. (2015) model has a very high angular resolution, whereas the radial resolution is limited by uncertainties on photometric distances. In principle our prior distribution is losing all the details contained in the angular variations, first because we start with a coarse angular sampling of 0.5 deg, and also because our full 3D inversion has a spatial resolution limited by the radial distance uncertainties.

Ideal inversions are based on a homogeneous spatial sampling of targets, i.e., a homogeneous volume density of targets and similar uncertainties on their locations at all distances, which allows us to retrieve structures of the same minimal spatial scale everywhere in space. But for most surveys, including Pan-STARRS, the achievable resolution is dependent on the distance to the Sun, especially owing to distance-dependent uncertainties on the star locations. The sampling of the data in this case does not allow the Nyquist-Shannon criterion to be fulfilled for the same minimal autocorrelation length everywhere; also, some of the energy attributable to high spatial frequencies is likely to be redistributed in low spatial frequencies, especially at large distances or in the radial directions when the sampling is weak. Therefore, we need an approach that allows us to retrieve only the large scales at large distances and all large and small scales at short distances. We implemented a hierarchical approach that allows us to achieve this goal in an iterative way by progressively updating the opacity model. Schematically, we first constructed a low spatial frequency map in a sphere with a large radius (here 5 kpc). In a subsequent step, we estimated spatial frequencies that are higher by a factor of 2, however this time in a smaller sphere (for instance 3 kpc) by removing data at large distances for which the available information does not make it possible to reach these frequencies using the Nyquist-Shannon criterion. The information at large distances is kept under the form of the prior, which is simply the distribution derived from the previous step. This procedure is repeated for increasingly high frequencies until their maxima compatible with the radial sampling (reached at small distances) are obtained. In practice we proceeded as follows:

1. The Pan-STARRS reddening data subsample (see Sect. 2.3) is converted into local opacities ρ 0 through derivation w.r.t. the radial distance R,

ρ 0 = δ E(B-V) /δ R . 2.
A spatial scale S of restitution of the opacity is fixed, starting with the (large) scale that can possibly be restored on the whole map. The average opacity is computed in a 3D grid of boxes of size S × S × S . The resulting average opacity is thus dependent on the scale. 3. Based on these average opacities, maps of smoothed opacities are computed by Bayesian interpolation with a smoothing kernel twice as large as S. 4. We then move on to a scale twice smaller. S /2 × S /2 × S /2 boxes are built and the average opacity is calculated for each box. The dynamics of the average opacity for this set of boxes is stronger. We suppress the boxes that do not fulfill the Nyquist-Shannon criterion for S /2. 5. A Bayesian interpolation is carried out again with a smaller smoothing kernel and using the previous map as prior. 6. Again, the scale is divided by two and steps 2) and 3) are performed again for this new scale, etc.

A planar cut along the Galactic plane in the resulting 3D prior is shown in Fig. 3. The Sun is at center and Galactic longitude increases counter-clockwise. As expected, far from the Sun we can only restore the very large scales. On the contrary, one can reach scales on the order of 100 pc within the first few hundreds parsecs. Because Pan-STARRS data are recorded from the Northern Hemisphere, the prior in the fourth (bottom-right) quadrant appears homogeneous at large distances from the Sun. This map can be used as a prior in the inversion of the individual lines of sight for which accurate distances are available, with the goal of improving estimates of opacity fluctuations and refining the distance assignment of the structures (see Sect.

Inversion of datasets

Inverting pre-and post-Gaia individual color excess data

In order to test the influence of the new Gaia parallax distances we inverted the reddening dataset described in Sect. 2.1 twice with exactly the same method, criteria, and parameters, but with different target distances. The first inversion (A) used the initial Hipparcos+photometric distances and errors as in LVV and the second (B) used Gaia-TGAS distances and associated errors when available, or unchanged values in the absence of Gaia result. Gaia DR1 parallaxes are about 20 times more numerous than Hipparcos parallaxes and about three to four times more accurate. These characteristics will evolve with the next releases, which will contain many more stars and reduced parallax uncertainties. As in LVV the prior dust opacity is a planeparallel homogenous distribution decreasing exponentially from the Galactic plane with a scale height of 200 pc. We also similarly retained for the inversion targets closer to the Galactic plane than 1000 pc and less distant than 2601 pc with relative errors on the distance smaller than 33%. The number of targets retained for the first inversion was 22 467 and among these 5106 were assigned their Hipparcos distance, and the rest were photometric. In the second inversion 23 444 targets were retained, with about 80% having a Gaia parallax. As the Gaia-DR1 uncertainties were 3-4 times smaller than Hipparcos uncertainties, the number of targets excluded for uncertain distances was significantly reduced. Importantly, the fraction of photometric distances decreased by a factor of four and Gaia distances started to be largely dominant.

The values of the χ 2 per target are computed from the differences between the observed color excesses and the color excesses computed by integration of the model 3D distribution along the target lines of sight. These values are reported in Table 2 for both the prior and final distribution. The value of χ 2 for the prior distribution is significantly higher for the second inversion, reflecting the fact that error bars entering its computation are on average smaller because of the reduced uncertainties on Gaia-DR1 distances, especially by comparison with previous uncertainties on the photometric distances. On the contrary, the final χ 2 is lower despite reduced error bars, demonstrating that the found distribution is more compatible with the new distances than with the previous distances.

Panels a and b (top left and right) in Fig. 4 show planar cuts in the two inverted opacity distributions A and B. The plane is parallel to the Galactic plane and contains the Sun. The pre-Gaia inverted distribution is not exactly the same as in LVV because of the removal of some targets and the introduction of the new method to treat outliers (see Sect. 3.1). The difference between the two pre-and post-Gaia distributions is visible: on average the structures are more compact. Some elongated structures diminished owing to the reduction of distance uncertainties, as in the third and fourth quadrants (bottom left and right); some others A65, page 7 of 17 appeared, which may be due to contradictions between new Gaia distances and the remaining photometric distances. The latter regions will have to be carefully studied once all targets benefit from Gaia parallaxes. The local bubble keeps its global shape, but looks slightly more complex owing to the higher compactness of structures. At larger distance the most conspicuous difference appears in the first quadrant, in particular in the Cygnus Rift region. At l ≃ 70 • the Rift is located significantly farther away in the new inversion, at about 800 pc instead of ≃600 pc, interestingly now in agreement with the Green et al. (2015) distance assignment. We knew that this was a region of strong oscillations in the first inversion; these oscillations have now disappeared. In the second quadrant there are substantial changes and we start to sketch a ring of clouds, more specifically the very large structure located from 100 to 400 pc is decomposed into two smaller structures.

Inversion of combined photometric and DIB-based color excess data

The same inversion code was applied to the merged catalog of photometric and DIB-based reddening estimates (inversion C).

As can be seen in Table 2, the introduction of the APOGEE targets reduces both the initial and final χ 2 per point. The former decrease means that error bars on the DIB-based estimated E(B -V)s are not underestimated. The latter decrease strongly suggests that the APOGEE targets do not introduce contradictory data and their inclusion produces a reasonable solution. In comparing the new Galactic plane cut in Fig. 4 and the distribution C at the bottom left with the previous inversion with photometric data alone (distribution B, top right), we can clearly see some changes; these changes are particularly apparent in the second quadrant, where some decoupling or fragmentation of large structures into distinct cloud complexes along with new structures at large distances are visible. Since 14% of the APOGEE targets are closer than 20 pc from the Galactic plane, are highly concentrated in this quadrant, and are more distant that the LVV targets, it is not surprising to find most changes there. The fourth quadrant does not change because the APOGEE catalog adds targets observed from the Northern Hemisphere. Since it is the first time that such measurements of different types are used, which could trigger incoherence in the inversion, it is mandatory to investigate the observed changes in all areas in order to validate this new technique. Four vertical planar cuts in the 3D opacity map are shown in Fig. 9 (Orion region), Fig. 10 (anticenter direction), Fig. 11 (Cepheus region), and Fig. 12 (Perseus region). The Perseus region is discussed in detail in Sect. 4.3. The images show, in the top panel, the B opacity distribution results and, in the middle panel, the C opacity distribution after inclusion of APOGEE data. In principle, the effect of adding coherent data in poorly covered areas should result in the transformation of wide structures into smaller clumps and/or in the appearance of new structures, especially at large distances in locations where there was initially no targets.

The Orion region is well suited for comparisons owing to the existence in similar proportions of targets of the two types beyond 150 pc. There are ≃750 lines of sight from the photometric catalog and ≃375 from the APOGEE catalog within the longitude interval l = (208 : 218 • ). As shown in Fig. 9, the two types of targets are spatially well mixed. If the data were not compatible we would observe radially elongated and/or wide structures in the middle map that were absent in the top. Instead, there are no significant changes, which demonstrates that the two datasets trace the same structures. Indeed, the figure shows that the APOGEE data reinforce the dense structures at 500 and 600 pc, at distances poorly covered by targets with photometric measurements.

Another region suited to a further check is the anticenter region. At variance with the Orion area, the LVV targets are concentrated in the first 100 pc, while APOGEE introduces many more distant targets. Figure 10 shows clearly that this addition allows us to map new clouds beyond 300 pc, in particular to transform a unique wide structure at ≃450 pc into at least three structures located between 300 and 700 pc, confirming that infrared data such as APOGEE add valuable information in areas where stars are already reddened by close clouds. The structures at small distances already seen in the first map (inversion B) remain in the second map but appear more compact with their relative opacities somewhat changed. Because the sightlines to the distant targets cross the nearby clouds, contributing to their mapping, the increased compactness of the foreground structure shows that the new data do not contradict the old data and instead help shape the close clouds.

We finally consider the Cepheus region in the second quadrant (Fig. 11). There are several clouds distributed in the same region of the sky, which is a very good test for the inversions. Here, the number of targets in the longitude interval l = (105 • ; 115 • ) increases from 390 to 664. It can be seen that the large structure at distances between 200 and 300 pc and at heights above the Galactic plane between 0 and 150 pc is now more clearly decomposed into three cores and an additional fourth cloud just below the Galactic plane at ≃300 pc. This last structure below the Galactic plane had apparently been erroneously associated spatially with a more distant and wide structure at 500 pc. As a result of Gaia distances and additional targets, its location has now been better defined. Indeed, the final distribution D (bottom panel of Fig. 11 and Sect. 4.3) confirms this new location. Behind those structures a hole as well as two different more distant clouds are revealed near the Galactic plane. The large structure located at about 400 pc in distribution A, which was derived from only a few targets, is now much better defined and significantly displaced farther away at about 500 pc. This again shows that using infrared data enables the mapping of distributed clouds and holes between them.

Inversion of merged data using the Pan-STARRS-based prior 3D distribution

We performed an additional inversion (inversion D) of the set of LVV+ APOGEE targets, this time replacing the plane-parallel homogeneous prior with the large-scale distribution described in Sect. 3.2, based on Green et al. (2015) results. This allowed us to test our new capacity of using an arbitrary 3D prior distribution. In the new prior, the smallest structures allowed are 100 pc wide and the kernel sizes for the largest structures are 800 pc.

Table 2 shows that using the new prior has the effect of reducing the initial χ 2 per point significantly and decreasing the final χ 2 too. This means that the data for individual sightlines are, not surprisingly, in better agreement with this more realistic prior. A comparison of panels c and d in Fig. 4 shows the differences introduced by using the new prior in the Galactic plane. As expected, the main variations are at large distance, especially beyond 1 kpc, while closer the changes are very small. The same effects are seen out of the Galactic plane, as shown in Fig. 9 compact structures with the minimum size allowed by the kernels and large-scale structures that mainly result from the Pan-STARRS prior. The small structures however should be considered with caution, as is discussed in the next section.

Bottom panel of Fig. 5 shows a zoom in the 1000 pc × 1000 pc central area. It shows numerous clouds and cavities and that the shape of the so-called local bubble is far from simple. It is well known that the interstellar matter within the first hundreds of parsecs is very complex and characterized by the series of clouds forming the Gould belt, a wide structure tilted w.r.t. the Galactic plane by about 20 • (e.g., Perrot & Grenier 2003). The origin of this structure is still a matter of debate (see, e.g., Lallement 2015 for a recent discussion and suggested origin). Two chimneys connect the local bubble to the northern and southern halos forming a hole whose axis is nearly perpendicular to the Gould belt plane; this peculiar structure is illustrated in Fig. 6. The figure shows for all directions the lineof-sight length that corresponds to an integrated color excess E(B -V) = 0.015 mag. When the distance required to reach this color excess is larger than the distance to the boundary of the computational volume, such as toward high Galactic latitudes, we used this distance multiplied by -1. The figure shows very clearly the local warp of IS matter as well as the chimneys connecting to the halo.

Uncertainties and comparison with other 3D measurements

Resolution and errors

Uncertainties on cloud distances and dust columns are strongly dependent on the distance to the Sun due to the combined effect of target space density decrease and target distance uncertainty increase. Additionally, uncertainties vary from one region to the other since databases have strong biases. Theoretically the errors may be calculated for each point in space along with the Bayesian inversion, but this kind of evaluation is very expensive in computation time (Sale & Magorrian 2014). On the other hand less precise error estimates for the distance to the structures and the amount of integrated reddening may be obtained by means of simpler approaches. We present these approaches here.

In the frame of a first simple approach we assumed that the error in distance varies as the achievable resolution, i.e., depends on the target space density. We choose as an estimate of the error at each point the size of the smallest sphere around this point that contains at least 10 targets. Another simpler and computationally faster approach is to calculate again in each point the target density ρ (in pc -3 ) in a cube of a given size centered on this point and use (1/ρ) 1/3 as the error estimate. This second technique is less precise because we keep the same volume everywhere in an arbitrary way. The results for the Galactic plane are shown in Fig. 13. The results from both methods are not markedly different and as expected there is a strong increase of the distance uncertainty from the center to the peripheral regions. The error can be compared with a similar estimate by LVV: it can be seen that the addition of the APOGEE targets improves the resolution in a number of regions from the first to third quadrant. The A65, page 12 of 17 As already mentioned in LVV, estimating the error on the local dust opacity is meaningless because by principle the clouds have a minimum size and as a consequence the dust is distributed in larger volumes in the model compared to the volumes of the actual, clumpy clouds. This spreading of the opacity in volumes that are wider than the volumes of the actual clouds introduces systematic underestimates of the opacities and integrated reddenings in the cloud cores. As a result, this systematic error is expected to be small for sightlines that do not intercept small, dense cores, but may reach high values in highly reddened regions in case the reddening is generated by one or a few small structures. We come back to this point in Sect. 6. On the other hand, it is possible to give rough estimates of the errors on the reddening (integrated opacity) that are linked to the dataset, independently of the errors due to the assumed cloud minimum size. To do this, for every point in our 3D map we selected all targets contained in a cube of specific size around the considered point, and for those targets we calculated the mean difference between the data and model color excess. Figure 14 shows the results for the Galactic plane with a cube of 50 × 50 × 50 pc 3 ; in case the cube contains less than 10 targets we use a volume of 200 × 200 × 200 pc 3 . Uncertainties on distances and color excess will be available on our website and displayed interactively. 1). These authors have mapped the clouds cores with a very high angular resolution, while our inversion produces much wider structures, however it is interesting to use this catalog for a comparison of cloud locations.

Molecular clouds catalogs

In Figs. 9, 12, and the bottom panel of Fig. 11, we superimposed the opacity distribution resulting from our D inversion and the position of the cloud cores identified by Schlafly et al. (2014) with associated error bars on their positions. For the Orion region (galactic longitude = 212 • ; Fig. 9), these authors found a sequence of clouds distributed from 400 pc to 600 pc, which are distances similar to the structures appearing after the inversion. In the case of Perseus (galactic longitude = 158 • ; Fig. 12), our map also indicates the same distances. In the case of the Cepheus region (galactic longitude = 110 • ; Fig. 11) the inversion produces two large, opaque structures above the Galactic plane, the first between 250-300 pc and the second between 700-800 pc. Two groups of high latitude molecular clouds have also been mapped in this region by Schlafly et al. (2014). The closest group is in reasonable agreement with our mapped structures, while the second is at somewhat larger distances. However, in this case our targets are very scarce at high latitude (see upper and middle panels in Fig. 11) and more data are needed to allow a comparison.

Soft X-ray emitting cavities as signatures of nearby recent supernovae

Following the work of Puspitarini et al. (2014) we searched for cavities in our 3D maps, which can be counterparts of supernova remnants (SNRs) or massive stellar winds. Such cavities are detected through their soft X-ray emission in ROSAT maps.

Figure 15 shows four vertical cuts in the 3D distribution along meridian lines crossing the four ROSAT 3/4 keV bright spots labeled 1 to 4 by Puspitarini et al. (2014). The coordinates of those X-ray spots are (l, b) = (-30

• , +14 • ), (-18 • , +18 • ), (-5 • , +9 • )
, and (-8 • , -10 • ). In the four cases, we see that at the latitude of the bright spot centers there are cavities surrounded by dense clouds, which is the typical configuration for moderately old SNRs. Moreover, these cavities are not separated from the Sun by opaque clouds, which is a condition for their detection in soft X-rays, since million K gas soft X-rays photons are easily absorbed; more precisely, the cavities must have foreground extinction that is lower than E(B -V) = 0.1 to be easily detected.

Compared to the previous search using LVV maps, the cavities are now better defined. Interestingly, the two cavities seen in the maps at (l, b, d) = (-18 2016), and at similar distances. These authors have used the signature of 60 Fe in deep-sea crusts as a sign of recent (about 2.2 million years ago) explosions of supernovae in the solar neighborhood, and, based on proper motions and ages of the young stellar groups, they computed the most probable present locations of the latest explosions. Given the uncertainties associated with their statistical modeling, the small differences between our cavities 1 and 2 and their results is negligible, and these identifications reinforce their model and the 60 Fe measurements and interpretations.

Conclusions and perspectives

We updated and improved our previous 3D maps of the local interstellar dust, based on the Bayesian inversion of photometrybased color excess measurements, in several ways. Our goal is to analyze the influence of the use of new Gaia parallaxes and to test two additional techniques aimed at providing more accurate 3D maps. First, the merging of photometric color excesses with color excesses estimated from diffuse interstellar band absorptions and, second, the use of a non-homogeneous large-scale prior distribution deduced from massive photometric surveys. The first technique increases the number of sightlines available for the inversion and, in turn, the resolution. The second work aims at testing hierarchical techniques to be used for Gaia photometric data in the future .

-We replaced ≃5100 Hipparcos and ≃17 400 photometric distance measurements with ≃18 700 new Gaia-DR1 available parallactic distances and ≃only 4700 remaining photometric distances. In the resulting maps some elongated structures have been suppressed, which demonstrates that such features were artefacts owing to the lower accuracy of former distances. Significant changes are also seen in specific areas, in particular the Cygnus Rift.

-We extracted by means of a dedicated profile-fitting technique a series of ≃5000 equivalent widths of 15 273 Å diffuse interstellar bands from SDSS/APOGEE high-resolution spectra of targets possessing a Gaia parallax and converted their equivalent widths into E(B -V) color excesses. We allowed for an additional 50% uncertainty to take into account the absence of a strict proportionality between the DIB EW and the color excess E(B -V). We then performed a new inversion using the combination of the previous reddening data and the DIB-based reddening estimates. In general the APOGEE targets are more distant than the previous stars, which results in more extended maps. We do not find any contradiction between previous and new maps in regions where both types of targets are present in similar numbers. Instead, a number of structures that were broad and had low opacities are assigned more complex shapes and denser cores, which suggests that the DIB-based color excess estimates can enter the Bayesian inversion and be used to get better constraints on the cloud locations. This has important consequences because all spectroscopic surveys provide DIB measurements that may help locate the interstellar clouds, especially if all targets have precise parallax measurements. -We used a subsample of Green et al. (2015) Pan-STARRS reddening estimates to build a low-resolution 3D map by means of a new hierarchical Bayesian interpolation method. This map is used as a prior during the Bayesian inversion of the individual data instead of a homogeneous distribution. We see that such a prior complements well the low distance map by adding spatially averaged large structures farther away. This means that data from statistical methods applied to massive photometric surveys and also future Gaia data may be used in conjunction with the databases of individual sightline data.

The resulting 3D maps are significantly improved with respect to the previous maps thanks to the use of Gaia parallaxes, the additional DIB database, and the use of a prior. This is demonstrated by the residuals, and especially by the increased compactness of A65, page 14 of 17 the structures and the disappearance or decrease of some of the elongated artifacts due to contradictory distances (the fingers of god) or lack of targets. Many of these artifacts remain, however, and are expected to disappear when all targets are assigned an accurate distance. In addition, the comparisons with other cloud location assignments and X-ray emitting cavities are encouraging. This shows that future Gaia parallax catalogs should considerably help the 3D mapping by allowing the use of a much larger number of targets. Merging photometric and DIB-based color excess estimates does not reveal any contradictory information and instead improves the maps, especially beyond foreground dense clouds. This opens the perspective of using the massive amounts of DIB data that are expected to be provided from high-resolution spectroscopic surveys. Finally, the use of a Pan-STARRS based prior demonstrates the feasibility of hierarchical methods for the inversion based on different datasets or massive amounts of data.

Our distributions are particularly useful at short distances, as they show how cloud complexes and cavities are spatially organized. In this respect, they are also appropriate tools for comparing with multiwavelength emission data, understanding environments of specific objects, computing particle or photon propagation modeled distributions, and searching for unreddened areas in case of stellar photometric model calibrations. In terms of angular resolution, they cannot compete with the distributions based on massive surveys. Especially, owing to the imposed cloud minimum size, opacities, and integrated reddenings toward the cores of the dense clouds can be strongly underestimated. In such nearby, opaque regions a valid approach may be the combination of both types of information, from high angular resolution maps, on the one hand, and from our 3D structure, on the other.

Vertical planar cuts in the 3D distribution are given in Appendix A at longitude intervals of 30 • . Our final map is available at http://stilism.obspm.fr in its entirety and online tools allow one to obtain reddening curves estimated through integration in the 3D opacity distribution along with rough estimates of the uncertainties on distances of the structures and reddening. We plan to update the maps and improve online tools when additional data are included in the inversion.

First 3D maps based on APOGEE DR14 DIB catalog

Based on the latest APOGEE DR14 catalog of 124 064 DIB measurements presented in Chapter 3, 3D maps were performed using a full 3D Bayesian tomographic technique. The inversion technique is similar to the one described in Lallement et al. (2018b). Since the inversion code inverts color excesses, the DIB strengths were converted in color excesses using the average EW-A V relationship (Eq. 5.1) established in Chapter 5.

Similarly to the analysis by Capitanio et al. ( 2017), we used an analytical prior distribution. For distances we made use of the recently published Data Release 2 (DR12, Arenou et al. 2018) when parallax distances have smaller uncertainties than those associated to the photometric derivation. Photometric distances are those estimated by [START_REF] Queiroz | StarHorse: a Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars[END_REF]. For the inversion, we restricted our sample to targets with distances shorter than 2600 pc and with a distance from the Plane < 600 pc and we discarded those with relative errors on distance > 33%. By applying these criteria, we obtained a sample of ∼ 40 000 stars with color excess estimates and distances. From the resulting catalog, two inversions were made simultaneously and separately. On the one hand, a 3D inversion using color excesses estimated from the DIB strengths and a second one using the color excess of the Starhorse catalog of extinctions1 , inversions are made for exactly the same targets distribution on the sky and the same distances. The main question is how far the distribution of the DIB carriers is similar to the one of the nanometric dust grains (responsible for the extinction).

In practice, the inversion was done in two steps, for the targets in the range of longitudes 0 • ≤ l ≤ 145 • and then 135 • ≤ l ≤ 360 • . This is justified by the fact that the inversion code is not yet suitable for managing such large datasets. Fig. 45 shows a comparison between the two resulting 3D maps of the galactic plane based on color excess derived from DIBs and extinctions respectively. Note that for this first attempt we do not expect precise and complete maps and this is only an exercise. As a matter of fact the targets are far from being uniformly distributed on the sky as shown in Fig. 44, instead they are concentrated in a limited number of observational fields. Moreover, most stars are located at distances greater than 500 pc. However, we can make some considerations from this preliminary analysis: in both 3D distributions shown in Fig. 45 we can identify the Local Bubble and the GSH238+00+09 cavity, a 500 pc wide empty region in the third quadrant. Both regions are characterized by a very low density of matter. However, the dust-based map shows a stronger depletion than the DIB-based map. This may indicate that the DIB species follow the gas distribution rather 4. DIBs as tracers of the nearby ISM Figure 44 The APOGEE target distribution retained for the inversion in galactic coordinate system.

Figure 45 Left) The 3D map of the Galactic plane derived from the DIBs catalog. Right) The same for the 3D map derived from photometric calibration.

Color scale is mag pc -1 . Courtesy: Letizia Capitanio.

than the dust concentration. A detailed study of the DIB scale height is in progress.

Conclusion

We have compared our DIB detections in the Taurus-Perseus region with the distribution of dust as traced by Planck, finding a good agreement between the strength/existence of the DIBs and the location of the clouds in the plane of the sky. Likewise, we showed how the use of series of stars with known distances allows to assign distances to the clouds. One more step was the application of inversion methods to the DIBs to recover the 3D structure, as already attempted based on optical absorption lines and extinction (e.g. Methods We compared our DIB measurements with the stellar extinctions A V from the Starhorse database. We then compared the resulting DIB-extinction ratio with the dust optical depth derived from Planck data, globally and also separately for nearby off-Plane cloud complexes. Results. Using the new catalog of 124 064 DIB measurements, we were able to revisit the correlation between DIB strength and dust reddening. The new data reveal clearly that the sky-averaged 15 273 Å DIB strength is linearly correlated with A V over two orders as reported by earlier studies but leveling off with respect to extinction for highly reddened lines-of sight behind dense clouds. The comparison with Planck individual optical depths reveals in a conspicuous way this DIB depletion in the dense cores and shows it applies to all off-Plane dense clouds. Using selected targets located beyond the Orion, Taurus and Cepheus clouds, we derived empirical relationships between the DIB to extinction ratio and the Planck dust optical depth for the three cloud 91 5. Conditions for DIB formation: a new view on the "skin effect" complexes. Their average is similar to the DIB carrier depletion measured in the dark cloud Barnard 68. Conclusions. APOGEE measurements confirm the ubiquity of the 15 273 Å DIB carrier decrease with respect to dust grains in dense cloud cores, in a manner that can be empirically related to the dust optical depth reached in the cloud. They also show that the ratio between the DIB equivalent width and the extinction A V for sightlines with τ(353GHz) 2 ×10 -5 that do not contain dense molecular gas is about four times higher than the constant limit towards which the ratio tends for very long sightlines with many diffuse and dense phases distributed in distance.

Caveat: In this Chapter, the usage of a new stellar modelling method prompted us to correct one of the EW15273 -A V relation established using this DIB's catalogs (Eqs. 5.1 and 5.2). This EW15273 -A V relation (Eq. 5. 2) is used throughout the manuscript, in prior publications, to derive a variety of physical quantities (Chaps. 4 and 6) or to guide to DIB profile modelling process (Chaps. 2, 3 and 7). The reader must be aware that this correction might have some implications on some of the results of Chaps. 4 and 6. In Chapter 4, the conclusions will be slightly affected since we put a conservative error on the extinction associated with an EW (to account for the variability of this ratio). In Chapter 6, this relation was used to derive the average equivalent width per unit reddening for the three weaker NIR-DIBs listed in Table 62. In this case, it is easy to correct with a simple factor. The other conclusions (e.g. discussions on the skin effect) are not affected and there are no implications on the interpretation of these earlier studies.

Introduction

In spite of an apparent general correlation between the DIB strengths and the interstellar extinction or reddening, all efforts have failed up to confirm the hypothesis that dust particles are responsible for the absorption of DIBs. One can cite in particular the most stringent limitations on polarization reported by Cox et al. (2007a) who analyzed six strong DIBs (at 5780, 5797, 6196, 6284, 6379 and 6613 Å) and revealed no linear line polarization, a polarization expected for large grain-related carriers. The lack of correlation between DIB strengths and the FUV extinction precludes the small grainrelated carriers as well (Desert et al. 1995;[START_REF] Xiang | Diffuse Interstellar Bands and the Ultraviolet Extinction Curves: The Missing Link Revisited[END_REF]. DIB carriers are then believed to be large molecules in gas phase. The detection of substructures in the profile of some DIBs (as we will see in Chapter 10.1) supports the molecular nature of some DIB carriers.

It remains that the global correlation between most DIBs and optical extinction suggests that dust grains that cause this extinction are associated with/linked to DIB carriers in some way. However, the underlying mechanisms and role of DIB carriers in the multiple physico-chemical reaction chains that occur throughout the ISM life cycle are still unknown and many

Introduction

open questions remain to be answered: is the DIB carrier density related to the depletion onto the grains? are the grains the carrier formation site?

Fortunately, the new massive stellar spectroscopic surveys can now support statistical studies allowing comparisons between absorption by gaseous species (e.g. CaII, NaI, KI, C 2 , CH, CH + ), DIB carriers and dust that can help clarify their links. Such statistical studies of relationships between DIBs, gas and dust grains as well as ratios between pairs of diffuse bands contain precious information on the molecular content and the evolution of macromolecules and grains in response to the physical properties, the radiation field, the abundances in the ISM, and the grain size distribution. A hierarchy of DIBs ordered by increasing sensitivity to the ionizing field is in progress (e.g. Ensor et al. 2017a). The link with the ionizing field is particularly interesting as it is related to the depth within the clouds, and thus to the grain properties.

An important observational clue is that some of the strongest optical DIBs have shown to level off with respect to dust reddening for lines-of-sight that probe denser molecular cores, rather than the diffuse edges, of interstellar clouds. Using the Lick Observatory scanning spectrophotometer, [START_REF] Wampler | Scanner Observations of λ 4430[END_REF] was the first to notice such an effect for the DIB at 4430 Å in the spectra of background dark clouds stars. It has been also seen by [START_REF] Snow | Diffuse interstellar band formation in dense clouds[END_REF] basing it on their study of DIB strengths at 4430, 5780, and 5797 Å in Sco-Oph and Per. They concluded that formation or subsistence of the carrier is inhibited in cloud interiors. Since then, the same effect has been commented in a number of studies [START_REF] Strom | an active region of star formation in the dark cloud Lynds 1630[END_REF][START_REF] Meyer | Observations of diffuse interstellar bands toward T Tauri stars[END_REF][START_REF] Wallerstein | The diffuse interstellar features at 5780 and 5797 A in star-formation regions // AJ[END_REF]Adamson et al. 1991;Herbig 1995). This behaviour has been referred to as the skin effect in the Herbig (1995) review. It is supported by the higher degree of correlation of the DIB strength with HI compared to molecular hydrogen (Friedman et al. 2011). Moreover, the discovery of unusually low DIBs in the spectra of the star HD52642 by [START_REF] Snow | Unusually Weak Diffuse Interstellar Bands toward HD 62542[END_REF] also confirmed such an interpretation, if, as it is likely, the diffuse outer layers of the dark cloud in front of the star have been stripped away. However, a particular class of DIBs, the so-called C2-DIBs (Thorburn et al. 2003), seems to be abundant in the dense molecular phase ( Ádámkovics et al. 2005). For recent reviews including the skin effect see Snow, McCall (2006a) and Snow (2014).

More recently, Lan et al. (2015) reported this behavior for many DIBs towards lines of sight intercepting high latitude molecular clouds using the Sloan Digital Sky Survey (SDSS) star, galaxy and quasar spectra. The authors concluded that the break seen at high E(B-V) values can be quantitatively characterized by the decrease of HI in dense clouds, and higher molecular fraction.

Finally, important additional findings on the skin effect have been brought by [START_REF] Fan | The Behavior of Selected Diffuse Interstellar Bands with Molecular Fraction in Diffuse Atomic and Molecular Clouds[END_REF]. The authors have performed a large study of eight different, strong optical DIBs and, especially, produced a statistically significant study of the DIB to extinction ratio as a function of various atomic and molecular tracers of the ISM phase. In particular, they found that the relationship between the DIB to extinction ratio and the molecular hydrogen fraction f(H 2 ) is characterized by a lambda-shaped curve with a peak at f(H 2 )≃0.3, definitely showing that the DIB carriers reside predominantly in this range of molecular fraction and tend to decrease in both fully molecular cores and atomic gas. As a result, this study reveals how the physical state of the gas controls the DIB to extinction ratio. Here, the term skin effect takes all this sense, since there is indeed a maximum volume density of DIB carriers for a particular molecular fraction, and a decrease above and below this specific fraction value. In what follows, we will only essentially address the depletion in the dense molecular phase, and for this reason we will refer to a depletion effect instead of a skin effet.

At least one of the near-infrared (NIR) DIBs, which allow to probe more heavily obscured regions, and thus denser (higher extinction) clouds, also displays a depletion in the dense phase. Using VLT/KMOS moderate resolution (R = 4000) near-infrared (H-band) spectra of 85 stars located behind the Barnard 68 dark globule -from the edge to the centre, a tomography of the 15 273 Å near-infrared DIB in an individual dark interstellar cloud has been performed for the first time (see Chapter 7). It has demonstrated the decrease of the 15 273 DIB carrier in the denser parts of such dark clouds.

Previous efforts to observe the skin effect either cover small regions around individual clouds, or have insufficient sample sizes to statistically constrain its amplitude. In this work, we expand the studies to a largely unbiased survey covering a significant fraction of the sky as presented in Chapter 3.

Our approach to the problem of the DIB-extinction link involves the use of over one hundred thousand individual lines of sight, focusing exclusively on the strongest DIB in the H-band, at 15 273 Å.

We begin the next Sec. 5.2 with a description of the public catalogs used in this study, including a brief discussion of the changes and novelties made in the various surveys. The description of the methods used for DIB extraction and the resulting catalog can be found in Sect. 3.3 of Chapter 3. Only a selective summary will be repeated here. In Sec. 5.3 we revisited the relationship between DIB strength and extinction showing a break at increasing extinction values. A special attention is paid to slight differences in the stellar models of the various pipelines, since correction for this variation is crucial to achieve a good DIB extraction, and we discuss the extinction estimates. Using our results in combination with existing extinction estimates and allsky dust map, we investigate in Sect. 5.4 both the relationship between their ratio and the dust optical measured by Planck. Our main conclusions are 5.2. Description of the public catalogs used in this study summarized in Sect. 5.5.

Description of the public catalogs used in this study

SDSS/APOGEE spectra

The detailed description of the SDSS/APOGEE Data release fourteen (DR14) spectra can be found in Chapter 3. For such targets we possess measurements of DIB strength, Doppler width and mean radial velocity (as described in the Chapter 3), as well as new estimates of the extinction and the distances by other groups. Our goal is to use the maximum number of those targets to search for the existence of a near-IR DIB saturation effect at high extinction, an effect that would be similar to what we have found for the same DIB in the dark cloud Barnard 68 (Chapter 7), and similar to what has been observed for optical DIBs (Lan et al. 2015).

Starhorse extinction and distance database

In order to study the link between the 15 273 Å DIB strength and the color excess (section 5.3), we made use of the stellar extinctions in the visible range A V calculated with the Bayesian Starhorse method developed by [START_REF] Santiago | Spectro-photometric distances to stars: A general purpose Bayesian approach[END_REF] and [START_REF] Queiroz | StarHorse: a Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars[END_REF]. The authors applied the Starhorse code to APOGEE DR 14 data and the associated stellar parameters derived from the Cannon method, therefore there is a consistency in using the results of the StarHorse technique (here version 1) applied to APOGEE DR14 and the Cannon DR14 model spectra.

Starhorse also contains spectrophotometric distances or parallax distances from Gaia TGAS (Gaia Collaboration et al. 2016) when available, as described in [START_REF] Majewski | The Apache Point Observatory Galactic Evolution Experiment[END_REF]. We also made use of these distances for statistical studies (see section 5.4). All catalogs are available to the community via the LineA web page1 .

Planck Galactic dust data

To trace Galactic dust properties in regions with DIB measurements and allow better comparisons with the interstellar dust along the sightlines, we made use of the Planck Legacy archival2 data. The Planck Collaboration used Planck and WMAP data to model the spectral energy distributions of the dust thermal emission and derive an all-sky map of the dust optical depth at 353 GHz, τ 353 and of the dust temperature, T D . Here we used the Planck 2015 results described in Planck Collaboration et al. (2016).

DIB and dust extinction

Taking advantage of evolutions and improvements of models and spectra, we have revisited the link between 15 273 Å DIB strength and the extinction.

To do so, we cross-matched our catalog of 15 273 Å DIBs (section 3.3) and the Starhorse Cannon database using TOPCAT [START_REF] Taylor | TOPCAT STIL: Starlink Table/VOTable Processing Software[END_REF]. We removed stars that are flagged with NUMMODELS BAD, EXTINCTION BAD and BRIGHT2MASS WARN in Starhorse catalog. We finally obtained a number of 124 064 stars with DIB detection and photometric extinction and distance.

Figure 51 shows the equivalent width of the DIB as a function of the extinction along the sightline to the target. As expected and firstly discovered by Zasowski et al. (2015) for this DIB, there is a strong correlation between these two quantities that extends over more than two orders of magnitude. We computed the median value of the EW in extinction bins of 1.6 magnitudes, and fitted this median value to a power-law relationship with the extinction bin centers. Using the new catalog we find the following relationship:

EW DIB = 0.056 × A 0.99±0.03 V ± 0.001 ( Å) (5.1)
for the range of A V =[0.55-13.35] mag. As explained above, our lower threshold is associated with EW error intervals. On the other hand, the upper limit for the linear fit is constrained by the limited number of targets with high extinctions and chosen to avoid poor statistics. This relationship is significantly different from the previous power-law formula derived by Zasowski et al. (2015) found over three orders of magnitude in both Av and EW:

EW DIB = 0.102 × A 1.01 V ( Å) (5.2)
We have investigated the reasons for this difference by comparing the EW results for the same individual stars and by visual inspection of the corresponding spectra and models. Fig. 52 shows such an individual comparison.

The figure shows that there are changes in the adjusted model spectra in many regions, and that by chance one of the most significant differences happens at the location of the 15 273 Å DIB. The Cannon stellar lines are deeper than the corresponding ASPCAP lines, and as a result the fitted DIB has a smaller EW. We further investigated the impact of these differences by cross-matching the catalog of Zasowski et al. (2015) and our catalog for the same target stars. The figure displays the average of the ratio between our determination and the one of Zasowski et al. (2015) in bins of increasing EWs. It is clearly seen that, the stronger the DIB EW, the smaller the difference between the two determinations. We subsequently conclude that a non negligible fraction of the difference between our two relationships can be attributed to the use of different synthetic stellar models. A second reason for the discrepant slopes is the use of different extinction estimates. Fig. 55 displays the RJCE and Cannon values of the extinction A V for the target stars listed in the table 1 of Zasowski et al. (2015). The average linear relationship between the two estimates is shown in the figure. According to it, RJCE values are on the order of 25% above the Cannon estimates for the set of stars. The two effects, the one due to the use of the Cannon extinction instead of the RJCE estimate on the one hand, and the one due to the slightly different Cannon spectrum on the other hand, both result in a global decrease of the EW to extinction ratio and explain why our DIB to extinction ratio is about 40% below the one found by Zasowski et al. (2015). Fig. 51 also shows that the linear relationship is no longer valid at high A V values, and that an increasing number of DIBs are significantly lower than predicted by this law. This is the so-called skin effect first detected by [START_REF] Snow | Diffuse interstellar band formation in dense clouds[END_REF] and then often found in the Figure 54 Average ratio between the EW derived in this work using the Cannon models and the EW derived by Zasowski et al. (2015) based on ASPCAP models, here in bins of EWs from our work. The ratio is increasing and reaches an asymptotic value on the order of 0.95 for large EWs above 300m Å.

spectra of highly reddened stars. This effect is here illustrated in Fig. 51 using averages of DIB EWs in bins of 1.6 mag of A V extinction. This choice of A V intervals is a compromise between the need for a minimum number of targets per bin and the maximum discretization over the whole range.

This depletion is better seen when zooming at the break region for high A V values (right part of Fig. 51). The ratio between the median value of column of DIB carrier and the extinction (column of grains) is clearly decreasing while A V is increasing. This is clearly due to an increasing number of sightlines with relatively weak DIBs. On the other hand, a large fraction of the sightlines have strong DIBs that remain quasi-proportional to the extinction.

It is interesting to compare the skin effect seen here in a large variety of sightlines with the specific case of a dark cloud, i.e. a quasi-perfect monocloud situation in which all the extinction is generated in a unique dense cloud. To do so, we have added to the figure the results obtained in Chapter

The DIB carrier depletion illustrated with Planck

A fraction of the APOGEE sightlines is out of the Plane and crosses the nearby clouds of the Gould belt. For those targets that are located behind the main clouds, the observed dust emission is entirely or almost entirely generated by the nearby clouds. For this reason it is interesting to investigate the link between the DIB to extinction ratio and the dust emission. One of the goals is to detect variations among the cloud complexes or conversely some general laws for the skin effect. rocally, in the more diffuse regions the ratio reaches values that are above the average ratio of ∼0.06 Å mag -1 . This is the first time such a clear visualization is possible, and it is allowed by the combination of the high-quality APOGEE data and the high quality and spatial resolution of the Planck maps. Similar figures, this time for the Taurus and Cepheus clouds are presented in Fig. 57 and Fig. 58. The same restrictions were applied on A V and EW, and the lower limits for the target distances were chosen as 1 kpc for Taurus and 1.3 kpc for Cepheus. The same striking association between cloud cores and low DIB-extinction ratios is immediately visible.

A visual inspection of the three figures does not reveal any marked difference between the three cloud associations. In order to quantify better the DIB level off we have represented in 5.4. The DIB carrier depletion illustrated with Planck relationship between EW/A V and τ 353 was found to be:

EW/A V = 7.6 × 10 -5 × τ -0.66±0.01 353 ( Åmag -1 ) (5.3)
We also performed the same adjustments for the three regions separately, this time taking into account uncertainties on the DIB to EW ratio (the fractional error on the ratio is estimated as the square root of the quadratic sum of fractional errors on the two terms). The adjustments are displayed in Fig 59 and the results were:

Taurus : EW/A V = 5.3 × 10 -5 × τ -0.70±0.01 353 ( Åmag -1 ) (5.4) Orion : EW/A V = 1.0 × 10 -4 × τ -0.65±0.02 353 ( Åmag -1 ) (5.5
)

Cepheus : EW/A V = 1.6 × 10 -4 × τ -0.59±0.02 353 ( Åmag -1 ) (5.6) (5.7)
These quantitative results show that the average relationships present some differences from one region to the other, with a slope varying from -0.70 to -0.56 and without overlap of the allowed intervals.

In a search for the origin of such differences, we have made use of threedimensional maps of the interstellar dust. Such maps are based on extinction measurements for stars at distributed distances and assign locations to the main cloud complexes [START_REF] Lallement | Gaia-2MASS 3D maps of Galactic interstellar dust within 3 kpc[END_REF]. of the strength of phase mixing. In case of Taurus, its smaller number of structures and smaller resulting phase mixing, the level off the DIB is more distinctly linked to the dust column and the slope of the decrease is stronger. This is the opposite for Cepheus. If our interpretation is correct, this implies that the actual DIB to extinction ratio decrease from the diffuse to the dense phase is even stronger than suggested by the slopes in Fig. 59.

Finally, we have attempted to use the Planck data and all APOGEE sightlines to study the DIB level off in all types of configurations. To do so we removed all conditions on the target distances and kept only the lower limits on A V 106 5.5. Conclusion

and EW imposed to avoid too large uncertainties on the DIB to extinction ratio. The total number of sightlines becomes 75 840. Fig. 511 shows the DIB to extinction ratio as a function of τ 353 . The Orion, Taurus and Cepheus targets discussed above are marked by black signs, while all other targets are color-coded according to the Galactic latitude. Interestingly, one can distinguish three different regimes: at high latitude, thus for low dust optical depth and mostly nearby clouds, the DIB-A V ratio apparently stabilizes, i.e. no longer increases with decreasing τ 353 . It reaches a limit on the order of ≃ 0.23 Å mag -1 . Such a value is probably representative of very diffuse regions located close to the boundary of the Local Cavity (see, e.g., maps from Capitanio et al. ( 2017)). The second regime corresponds to the trend observed for the off-Plane Taurus, Orion and Cepheus regions, as already discussed, however this time it also applies to a large number of targets within the Plane. The third regime corresponds to large optical depths and sightlines within the mid-plane (as indicated by the low values of Galactic latitude). Here the ratio is no longer depending on τ 353 and reaches a limit on the order of 0.05 Å mag -1 , a value very close to the average found from the global linear fit to the data. This value must correspond to a strong mixing of diffuse and dense phases all along the sightlines.

Superimposed in Fig. 511 are the Barnard 68 corresponding data points for the five regions delineated in Chapter 7 that span extinctions A V between 2 and 20 mag. To convert A V into τ 353 we used the average relationship A V ≃ 4.5 10 4 τ 353 , based on the classical A V to E(B-V) ratio of 3.1 and the average E(B-V) vs τ 353 relationship of [START_REF] Grenier | Cosmic-rays, gas, and dust in nearby anti-centre clouds. III. Dust extinction, emission, and grain properties[END_REF]. It can be seen from the Figure that these data points fall in the middle of the ensemble of data points for Taurus, Orion and Cepheus, i.e., again, the dark globule does not correspond to an exceptional situation in terms of the DIB carrier depletion. This is interesting since Barnard 68 is immersed in a large opaque region with only a few ionizing stars, in strong opposition to Orion.

Conclusion

We have compared our DIB measurements with their associated visual extinctions extracted from the Starhorse database. We revisited the DIB-A V relationship first reported by Zasowski et al. (2015) and found the DIB strength average to be linearly correlated with dust extinction over at least two orders of magnitude, however with a smaller equivalent width-to-extinction ratio than the one derived by these authors for the red giants and later by Elyajouri et al. (2016) for the hotter TSS targets: EW DIB = 0.056 × A 0.99±0.03 V ( Å). We showed how this variation is explained by the differences between the ASPCAP and Cannon models and by the use of Cannon A V extinctions instead of A V extinctions deduced from the RJCE technique.

It has also been found from the global DIB-extinction comparative study that the 15 273 Å DIB tends to weaken with respect to extinction in the spectra of stars situated behind dense regions. This break seen at high A V values is well known as the skin effect (Herbig 1995) and has been observed several times for optical DIBs. In the case of the 15 273 Å DIB, it was also observed recently in the B68 nearby dark cloud (Elyajouri et al. 2017) with a strength comparable to multiple sightlines from the present study. Very recently, it has been more specifically characterized and quantified for optical DIBs by [START_REF] Fan | The Behavior of Selected Diffuse Interstellar Bands with Molecular Fraction in Diffuse Atomic and Molecular Clouds[END_REF] who demonstrated the existence of a maximum carrier abundance with respect to dust grains for a molecular H 2 fraction on the order of 0.3.

With the goal of going one step further in the analysis of the DIB carrier depletion in dense cores, we investigated the link between the EW/A V ratio and the distribution of dust as traced by Planck. In particularly, the Taurus, Orion and Cepheus cloud complexes were studied using targets distant enough to be located behind the clouds. The depletion effect is spectacularly illustrated for those cloud complexes, thanks to the high quality of the APOGEE data and of the Planck maps. We have fitted an average relationship between the DIB A V ratio and the Planck optical depth τ 353 through a log-log linear formula: EW/A V = 7.6 × 10 -5 × τ -0.66±0.01 353 ( Å mag -1 ).

We have performed a separate study of the three cloud complexes in a search for potential differences in the DIB depletion effect. For the Taurus clouds (resp. Orion, Cepheus), and taking into account uncertainties on equivalent widths and extinction the coefficients of the log-log linear relationship are: (5.3 × 10 -5 ; -0.70 ± 0.01), (1.0 × 10 -4 ; -0.65 ± 0.02), (1.6 × 10 -4 ; -0.59 ± 0.02) respectively. Using 3D maps of the dust clouds obtained by inversion of stellar individual extinctions [START_REF] Lallement | Gaia-2MASS 3D maps of Galactic interstellar dust within 3 kpc[END_REF], we have shown that the decrease of the depletion gradient from Taurus to Cepheus is very likely due to a corresponding increasing number of structures encountered along the paths to the target stars behind the three complexes, and the subsequent increase of phase mixing. Our results provide first empirical average relationships between the 15 273 Å DIB carrier depletion in molecular clouds as a function of the dust optical depth.

Finally, we have used the entire dataset in an attempt to identify different depletion regimes for the APOGEE sightlines. We found that, not surprisingly, for large dust optical depths exceeding ∼ 10 -4 , i.e. for low latitude and extended sightlines, the DIB to A V ratio tends to the mean value of about 0.05-0.06 × A V that is characteristic of a strong mixing of diffuse and dense phases distributed along the extended paths. For intermediate τ 353 values, between ∼ 10 -5 and 10 -4 we clearly see the same depletion effect that is affecting the nearby off-Plane cloud complexes, now extended to mi-plane targets. Finally, for low values of τ 353 below 10 -5 , the DIB to extinction ratio 108 5.5. Conclusion seems to be constant at ∼ 0.2 × A V , a value significantly above than the strong mixing average. This high value corresponds to the nearby diffuse clouds at the periphery of the local cavity.

The origin of the DIB carrier depletion in dense molecular cores is still unclear. In the context of PAH-like carriers, UV radiation would certainly directly influence the charge state of PAHs (e.g. [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF][START_REF] Ruiterkamp | PAH charge state distribution and DIB carriers: Implications from the line of sight toward ¡ASTROBJ¿HD 147889¡/ASTROBJ¿[END_REF] and the resulting disappearance of some DIBs could simply be due to the evolution of the charge state from the outer to inner regions of the clouds. On the other hand, photo-processing and hydrogenation or dehydrogenation may enter in play (e.g. [START_REF] Vuong | Dehydrogenation of polycyclic aromatic hydrocarbons in the diffuse interstellar medium[END_REF]. More recently [START_REF] Bertaux | Diffuse interstellar bands carriers and cometary organic material ?[END_REF] suggested that a general carrier disappearance could additionally be due to coagulation of DIB carriers into aggregates and accretion onto dust grains, as predicted by [START_REF] Jones | Dust evolution, a global view: II. Top-down branching, nanoparticle fragmentation and the mystery of the diffuse interstellar band carriers[END_REF] in the context of new models of dust. It is well known that grains grow in compact cores, generating the so-called coreshine effect [START_REF] Lefèvre | Dust properties inside molecular clouds from coreshine modeling and observations[END_REF]) and grain coalescence to form aggregates may also involve macro-molecules associated with DIBs. Future detailed studies of the DIB carrier depletion in regions with differing radiation field, pressure, temperature and grain properties will hopefully help disentangling the different contributions. In this respect, empirical relationships involving dust emission such as those established here may complement observed links with the physical state of the gas such as those of found by [START_REF] Fan | The Behavior of Selected Diffuse Interstellar Bands with Molecular Fraction in Diffuse Atomic and Molecular Clouds[END_REF] and bring additional clues.

Chapter 6

Weak Near-infrared diffuse interstellar bands

Published:

"Near-infrared diffuse interstellar bands in APOGEE telluric standard star spectra" Elyajouri, M., Lallement, R., Monreal-Ibero, A., Capitanio, L., & Cox, N. L. J.

Astronomy & Astrophysics, Volume 600, id.A129, 15 pp.2017a.

Abstract

Information on the existence and properties of diffuse interstellar bands (DIBs) outside the optical domain is still limited. Additional infrared (IR) measurements and IR-optical correlative studies are needed to constrain DIB carriers and locate various absorbers in 3D maps of the interstellar matter. We extended our study of H-band DIBs in Apache Point Observatory Galactic Evolution Experiment (APOGEE) Telluric Standard Star (TSS) spectra. We used the strong λ15 273 band to select the most and least absorbed targets. We used individual spectra of the former subsample to extract weaker DIBs, and we searched the two stacked series for differences that could indicate additional bands. High-resolution NARVAL and SOPHIE optical spectra for a subsample of 55 TSS targets were additionally recorded for NIR/optical correlative studies. From the TSS spectra we extract a catalog of measurements of the poorly studied λλ15617, 15653, and 15673 DIBs in ≃300 sightlines, we obtain a first accurate determination of their rest wavelength and constrained their intrinsic width and shape. In addition, we studied the relationship between these weak bands and the strong λ15 273 DIB. We provide a first or second confirmation of several other weak DIBs that have been proposed based on different instruments, and we add new constraints on their widths and locations. We finally propose two new DIB candidates. We compared the strength of the λ15 273 absorptions with their optical counterparts λλ5780, 5797, 6196, 6283, and 6614. 113

Weak Near-infrared diffuse interstellar bands

Using the 5797-5780 ratio as a tracer of shielding against the radiation field, we showed that the λ15 273 DIB carrier is significantly more abundant in unshielded (σ-type) clouds, and it responds even more strongly than the λ5780 band carrier to the local ionizing field.

The optical spectra were collected by our team with the NARVAL spectrograph on the Bernard Lyot telescope (TBL) at Observatoire du Pic du Midi (CNRS/UPS), France, and with the SOPHIE spectrograph on the Observatoire de Haute-Provence (OHP) 1.93m telescope (CNRS/AMU), France.

Introduction

To date, ∼30 DIBs have been detected in the near-infrared (NIR; > 0.9 µm) [START_REF] Joblin | Detection of diffuse interstellar bands in the infrared[END_REF]Foing, Ehrenfreund 1994;[START_REF] Joblin | Probing the connection between PAHs and hydrogen (H, H 2) in the interstellar medium and in the laboratory // H2 in Space[END_REF][START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF]Cox et al. 2014;[START_REF] Hamano | Near-infrared Diffuse Interstellar Bands in 0[END_REF][START_REF] Hamano | Near Infrared Diffuse Interstellar Bands Toward the Cygnus OB2 Association[END_REF], and only one band (the λ15 273 DIB) has been extensively explored, based on the high-quality high spectral resolution and numerous APOGEE spectra (Zasowski et al. 2015). NIR DIBs are particularly useful since they allow us to make use of highly reddened target stars and explore, if present, the densest areas of the ISM. The exact number and relative strengths of the NIR DIBs provide further constraints on their carrier population. The Sloan Digital Sky Survey (SDSS)/APOGEE dataset offers a unique opportunity to extract NIR DIBs and study their properties. In particular, the smooth continua of the bright and early-type stars selected in each field to be used as standards for telluric line corrections (telluric standard stars, TSSs) make them ideal targets for DIB extraction.

Our work has two main aims. On the one hand, we present the results of an analysis of the APOGEE TSS spectra, devoted to the extraction and identification of weak NIR DIBs. The work is a continuation of the extraction of an extensive catalog of measurements of the strong λ15 273 DIB based on the same TSS data presented in Chapter 2, and makes use of these previous results. On the other hand, we explore the potential of the strongest IR DIB as tracer of the interstellar structure. The chapter is structured as follows: Sect. 6.2 contains a brief description of the datasets. In Sect. 6.3 we describe equivalent width and Doppler shift measurements of the λλ15617, 15653, and 15673 DIBs as well as their properties. Sect. 6.4 describes our exploratory method aiming at confirming (or not) the known weak NIR DIBs and at potentially identifying additional NIR DIBs. Sect. 6.5 presents the optical DIB measurements and the correlations between NIR and optical equivalent widths. Our main conclusions are summarized in Sect. 6.6. 6.2. Data 6.2 Data

APOGEE TSS data

This contribution is based on the products from APOGEE, which is one of the SDSS-III experiments (Eisenstein et al. 2011;[START_REF] Aihara | [END_REF]). Specifically, we used spectra from the SDSS data release 121 (DR12 Alam et al. 2015), which provides all the data taken between April 2011 and July 2014. Each individual spectrum covers from ∼15 100 Å, to ∼16 700 Å, at a resolution of R∼ 22 500. The TSSs are used to clean the spectra of the APOGEE targets from telluric absorption lines, including the TSSs themselves. They are the bluest stars on a given APOGEE plate with a magnitude in the range 5.5 ≤ H ≤ 11 mag, and are therefore hot and bright stars with spectra that are most often (but not always) featureless. These characteristics make them ideal targets to aim at detection of faint DIBs, as we intend here. On the other hand, being hotter than the main APOGEE targets, the TSSs do not have fully adjusted tailored synthetic spectra (García [START_REF] Pérez | ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline[END_REF]) (for a TSSs detailed description see Zasowski et al. 2013). The APOGEE products contain the TSS decontaminated spectra and synthetic stellar spectra that provide the main stellar line locations and relative depths and widths.

Both have been used in Chapter 2 to extract a catalog of λ15 273 DIB measurements for ≃ 6700 lines of sight. In continuity of our previous work, we restricted our analysis to the 6700 TSSs for which we detected the λ15 273 DIB. Throughout the analysis we use vacuum wavelengths for the infrared data.

New optical spectra

A subset of ∼ 60 target stars from the APOGEE TSS list described in Sect. 6.3 has been observed in the visible with NARVAL, the spectropolarimeter of the Bernard Lyot telescope (2m) at Pic du Midi observatory, used in highresolution spectroscopic mode (R≃ 80,000). For all data the signal-to-noise ratio (S/N) is between 50 and 100. Two targets were observed twice in order check the estimated uncertainties. An additional subset of five targets was observed with the SOPHIE spectrograph at the 1.93m telescope of the Haute-Provence Observatory at a resolving power R≃ 39,000. Because the targets have been selected for their good detections of the weak NIR DIBs in the APOGEE spectral range, they were expected to be strongly absorbed and possess a smooth continuum, which has been verified for all of them. The telluric absorption lines were removed in the λ6283 DIB spectral intervals using TAPAS model transmittances [START_REF] Bertaux | a webbased service of atmospheric transmission computation for astronomy[END_REF] In what follows, we describe the creation of the catalog and determine some of its properties.

Fitting method

To maximize the chances of detection of those three fainter DIBs, we use as starting point the 5124 spectra of the catalog in Chapter 2, which were classified as having a well-detected DIB at λ15 273 . After visual inspection, 308 spectra were retained because they display at least one of the three bands.

From the extracted DIB EWs and Eq. 3 in Sect. 5, they correspond to an average visual extinction Av=1.2. They are characterized by a very high S/N, with a lowest S/N of 135 and a very high average S/N of 700. This is due to a double selection effect: first, the TSSs are characterized by a higher than average S/N, being the bluest and brightest objects in the field. Second, the visual selection among the TSSs favors the best spectra. As we show below, for such remarkable data DIB extractions are essentially limited by the presence of telluric (and sometimes) stellar line contaminations and not by the noise. We then fit each TSS spectrum to a model made out of the product of several components as follows:

M λ = [S λ ] α × 3 ∏ i=1 DIB[σ, λ, D] × (1 + [A] × λ). (6.1)
The variables in this equation are described in detail below.

• [S λ ] α , an adjusted stellar spectrum: S λ is the initial stellar model provided by the APOGEE project. The scaling factor (α) is introduced in order to adjust the model stellar line depths to the data.

• DIB[σ, λ c , D], the DIB profile: It was modeled as a Gaussian function with three free parameters associated to its Gaussian RMS width (σ), central wavelength (λ c ) and depth (D) for each DIB. Here we fit the data for the three 6.3. Catalog of λλ15617, 15653, and 15673 DIBs DIBs simultaneously (i=1-3).

• (1 + [A] × λ), a 1-degree polynomial introduced to model as close as possible the continuum around the three DIBs. We selected a predefined spectral range for the fit restricted to the vicinity of the three DIBs [15 578 -15 689] Å to determine the above coefficients, the width σ, central wavelength λ c , and depth D for each one. Table 61 shows the fitting constraints. We fit the three features simultaneously using a unique stellar continuum (a unique scaling factor α), since they are close in wavelength. We note that there is no stellar Brackett line in this spectra region. Errors provided by APOGEE were used to mask the spectral ranges that are affected by artifacts due to imperfect sky emission correction or other sources of uncertainty. Representative examples illustrating our fitting procedure are shown in Fig. 61 and more are presented in appendix A. In each sightline, equivalent widths and stellar rest frame wavelength are determined from the best-fit parameters and are given in Table 65. We rejected those cases from the catalog where the fit failed due to low S/N, an inadequate stellar model, or most of the times very strong telluric contamination. This cleaning rejected more spectra for the broadest λ15653 DIB (46 rejections) compared to the narrower bands (13 and 0 rejections for the λλ15617 and 15673 bands respectively). [15668,15680] 4.5 ≥ 0.0001

Error estimates

We distinguish two sources of errors, one associated with the noise (δEW n ) at the DIB location, and one associated with the placement of the continuum (δEW c ). In the case of regularly distributed noise, that is, for the equivalent noise level on the sides of the DIB and at the DIB location, these errors can be treated as independent and are added quadratically. Because telluric line residuals and stellar features may be drastically different along the spectrum and from one spectrum to the other, we conservatively added the two errors.

Here, we used the following formulation:

δ EW = δEW n + δEW c = 2 √ 2σδ Depth + 2 √ 2σstdev(data -model). (6.2)
δ Depth is the uncertainty on the DIB depth that results from the Gaussian fit.

The approximate formula for the first term was derived using a series of 117 6. Weak Near-infrared diffuse interstellar bands simulations with varying Gaussian noise. The exact mathematical formulation is

√ π √ 2σδ Depth , that is, ≃ 1.8 √ 2σδ
Depth when the width is fixed during the fit. We conservatively replaced 1.8 by 2 to account for the partially free width. The quantity stdev(data-model) is the standard deviation in the two regions that define the continuum [15 578 -15 607] Å and [15 675 -15 683] Å. This second term again conservatively assumes that the continuum can be displaced by one standard deviation on both sides of the DIB. The mean values over the whole catalog of these two errors are on the same order. However, their relative values vary strongly from one spectrum to the other. For the spectra shown in Fig. 61 the second term is dominant since the continuum is very well fit, but there are opposite situations.

DIB characteristics

The λλ15617, 15653, and 15673 DIBs have been observed in only a few sightlines [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF]Cox et al. 2014). Here we benefit from the large 6.3. Catalog of λλ15617, 15653, and 15673 DIBs amount of measurements to improve their characterization.

Central wavelengths

A very precise determination of the rest wavelength of the strong λ15 273 DIB has been made by Zasowski et al. (2015) based on the whole APOGEE dataset. Combining this information with our previous measurements of the Doppler shifts of this strong DIB in the TSS spectra allows us to determine the rest wavelengths of the three weak DIBs in a relative way: for each spectrum we computed the difference between the fitted central wavelength of each weak DIB, that is, λ 15617 , λ 15653, or λ 15673 and the central wavelength of the λ15 273 DIB, λ 15 273 . While all DIB wavelengths vary from one star to the other due to the IS cloud motions and subsequent Doppler shifts, for a given sightline the Doppler shifts of all DIBs are the same, and as a result, the wavelength intervals between the DIB centroids (e.g., ∆λ 15617 = λ 15617λ 15 273 ) remain approximately constant (we assume that V r /c is negligible). For each of the three DIBs we computed the average of these wavelengths intervals for all spectra of the catalog and added to the difference the central value λ = 15272.42 Å of the strong DIB (Zasowski et al. 2015). The resulting three central wavelengths and estimated errors are listed in Table 62.

DIB widths and shapes

The large number of measurements for nearby targets allows us to improve estimates of the DIB widths and shapes. Table 62 lists the mean FWHM of all fitted Gaussian absorptions and Fig. 62 displays their distributions. Both show significant differences between the three DIBs, with a width decreasing by a factor 1.5 from the broadest band (λ15653) to the narrower (λ15673). Owing to the weakness of the DIBs and our selection of the most reddened sightlines, our distribution suffers from biases and it is not possible to derive the intrinsic widths simply from the histograms. However, it is possible to constrain the intrinsic widths to some extent. On one hand, the histograms are asymmetric with a shallower slope toward the high widths, indicating that in addition to broadening due to noise and spectra contamination, there is an additional kinematical broadening. This is particularly visible for the λλ15617 and 15653 bands. As a consequence, we can consider the histogram peak as an upper limit on the intrinsic width. On the other hand, the histograms show that there are only very few cases of DIBs narrower than ≃ 2 Å, and visual inspection shows that they correspond to large uncertainties. For these reasons we can safely assume that this value corresponds to a lower limit for all our sightlines.

As we discuss in Sect. 6.5 for the TSS targets the velocity spread of the intervening clouds is found to be small by comparison with the optical DIB widths, and this must be also true for the NIR DIBs. Therefore, the 2 Å cutoff 6. Weak Near-infrared diffuse interstellar bands is very likely the null-broadening low end of the histogram. This results in the reduced intervals for the intrinsic widths: 2 ≤ FWHM(15617) ≤ 4.4 Å, 2 ≤ FWHM(15653) ≤ 5.7 Å, and 2 ≤ FWHM(15673) ≤ 3.7 Å. In the future, more numerous measurements of spectra and velocity structures or highresolution single-cloud line-of-sight studies will hopefully better constrain the intrinsic widths. We used the stacked spectra described in Sect. 6.4 to derive the average shape of the λλ15 273, 15617, and 15673 bands. The λ15653 DIB was not considered here because it is quite strongly contaminated by a telluric emission. The average profiles are displayed in Fig. 63 and suggest that the λλ15617 and 15673 bands are slightly asymmetric, with a shallower slope of the red wing in a way similar to most optical DIBs. The asymmetry is stronger for the λ15617 band. A Gaussian fit to the λ15 273 band gives FWHM= 3.91 Å, a value slightly lower than the peak value FWHM= 4.12 Å (or σ = 1.75 Å) measured by Zasowski et al. (2015) for high-latitude sightlines. Our use of bright, often nearby targets may explain that our average width is less affected by cloud velocity dispersion and is closer to the intrinsic width. Gaussian fits to the averaged λλ15617 and 15673 profiles provide FHWM= 4.2 and 3.1 Å, respectively, which corresponds to the maxima in their histograms in Fig 63.

Search for the weakest bands

Table 63 lists all published DIB detections and candidates in the APOGEE wavelength range (except for the strong λ15 273 DIB), based on the earlier works [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF]Cox et al. 2014). For all bands the number of de-121 6. Weak Near-infrared diffuse interstellar bands tections is very small and some are quite uncertain. We used the TSS spectra to improve the characterization of these previous detections and tentatively identify new candidates. To do so, we assumed that on average, all DIBs are positively correlated with the strong λ15 273 band and built two average spectra, one that we call strong-15 273, which is made of spectra showing a λ15 273 band with a high EW, and one that we call weak-15 273, which is made of spectra showing a weak EW. The former strong-15 273 list starts with our selection of 308 targets of Sect. 6.3 that correspond to highly reddened targets. We selected in this list the spectra for which the standard deviation between the data and the fitted model in the whole 15263-15558 Å spectral interval is smaller than 1%, and the 16895 Å stellar line depth is larger than 3%. This second criterion may appear surprising at first sight because it excludes the hottest target stars, but it ensures an excellent modeling of all stellar lines. This appeared to be crucial for the stacking. A total of 164 spectra were retained. For the weak-15 273 list we started with the subsample of the full TSS catalog of Chapter 2 that corresponded to the detection of weak DIBs (flag 5). We extracted from this subsample the series of data that meets the same signal quality requirements as for the strong DIBs above. Forty spectra were retained following these criteria. The S/N for the two stacked spectra reaches ∼2000-2500 in the clean areas, allowing in principle to detect absorptions as weak as 0.05%. For both subsets the spectra were shifted to a common rest frame (the rest frame of the first target) and were then stacked. The strong-15 273 stacked spectrum was used to determine the central wavelengths and average profiles (see the λλ15 273, 15617, and 15673 DIB profiles in the previous section and Table 62).

The two stacked spectra are displayed in Fig. 64. The figure allows us to compare them in all spectral regions in a search for departures that indicate an absorption feature, based on the assumption that all absorptions are at least partly correlated. The strong-15 273 spectrum shows significant departures from the weak-15 273 spectrum at the locations of several of the detected DIBs, which confirms their existence: the obvious λ15 273, the three DIBs λλ15617, 15653, and 15673 discussed in the previous section, and the four bands λλ15990, 16232, 16573, and 16585. For all of them, except for the λ15653 band, which is strongly contaminated by telluric lines and the λ16232 band, which appears to be very broad, we have fitted a continuum around each detected or potential DIB in the strong-15 273 spectrum and 122 6.4. Search for the weakest bands 63. For the broad λ16232 band we show the difference between the two stacked spectra and the corresponding estimated value of its width. The λ16232 width is found to be significantly smaller than earlier results of [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF].

The comparison between the two stacked spectra does not reveal any marked difference at the location of the tentative λ15225 DIB detected by [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF], in agreement with the absence of detection by Cox et al. (2014). However, we detect a non-negligible depression at 15235 Å, and we suggest that it is a potential DIB candidate. We do not detect any feature at 16596 Å, contrary to [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF] and Cox et al. (2014). We note that this spectral region corresponds to a strong telluric doublet. Finally, we detect a potential candidate at 16769 Å, as shown in Fig 66. This spectral region corresponds to the left wing of a broad stellar line whose continuum is fit as illustrated in the figure.

The detectability of new weak diffuse bands strongly depends on the spectral interval. It is much lower in intervals contaminated by telluric residuals and at the location of stellar lines. In the cleanest areas, a DIB with EW/E(B-V) = 11 m Å mag -1 such as the λ15990 DIB can be detected using the stacked spectra, as shown in Fig. 64. However, such a DIB represents here a limit for the method, as can be estimated visually from the figure: DIBs weaker than EW/E(B-V) ∼ 10 m Å mag -1 and widths on the order of 1-2 Å may remain undetected in the clean areas of the data. Stronger DIBs can also remain undetected in contaminated areas. • Like all correlations, they contain informations on the carriers and may reveal families of DIBs. Today, NIR DIB-DIB correlation studies are still limited by the small number of NIR DIB measurements for transitions other than the λ15 273 APOGEE main DIB. Cox et al. (2014) studied the correlations among three NIR DIBs (λλ11800, 13180, and 15 273) and found correlation coefficients of 0.90 and 0.97. On the other hand, their comparisons with optical DIBs led to a wider range of coefficients, from 0.83 to 0.98. [START_REF] Hamano | Near-infrared Diffuse Interstellar Bands in 0[END_REF][START_REF] Hamano | Near Infrared Diffuse Interstellar Bands Toward the Cygnus OB2 Association[END_REF] performed an extensive study of NIR DIB correlations for the 20 bands within the [0.91 -1.32] µm spectral range and found widely distributed correlation coefficients, ranging from 0.45 to 0.99 for NIR-NIR relationships, and ranging from 0.39 to 0.95 for the relationships between four NIR DIBs and eight optical bands.

• DIB -DIB correlations and DIB correlations with the reddening or the gas column are important when the DIBs are to be used for mapping purposes. In principle, any ISM tracer, including DIBs, can be used to assign distances to clouds based on gradients, but the links with other tracers are fundamental for deriving physical quantities.

• Optical DIBs observations cannot be used to trace highly or extremely reddened LOS. In contrast, NIR DIBs may be very useful to have a 125 6. Weak Near-infrared diffuse interstellar bands proxy of the amount of matter that may be able to penetrate these regions.

Here we have used the APOGEE TSS data and ground-based measurements to study the links between the NIR DIBs we could extract and the links between the APOGEE strong DIB and optical bands in more detail.

NIR-NIR DIB correlations

Figure 67 shows a comparison of the three DIBs λλ15617, 15653, and 15673 with the stronger λ15 273 band. Despite the large uncertainties, each of the three DIBs is positively correlated with this band. We performed a proportional linear fit using the orthogonal distance regression (ODR) method to take uncertainties on two compared quantities into account. The slopes we found are indicated in the figure. These slopes are used to derive the average equivalent width per unit reddening listed in Table 62, using as a reference the value derived by Zasowski et al. (2015) for the λ15 273 DIB, namely:

EW DIB = 102 m Å × A 1.01±0.01 V , ( 6

Extraction of optical DIBs from APOGEE TSS follow-up observations

The equivalent width for the optical DIBs was measured using a modified version of the fitting method described in [START_REF] Puspitarini | Automated measurements of diffuse interstellar bands in early-type star spectra[END_REF]). The parameter determination was split into two steps. First, we determined the shift of the DIB by cross-correlation of the observed spectrum with an empirically determined template for the DIB [START_REF] Puspitarini | Automated measurements of diffuse interstellar bands in early-type star spectra[END_REF]Raimond et al. 2012). Then, the shifted spectrum was fit to determine the coefficients associated to the strength of the DIB and small adjustements to the continuum. We note that the DIB at λ6283 is in a spectral region with heavy atmospheric absorption. For this specific DIB, we therefore estimated and removed the telluric absoption using TAPAS2 [START_REF] Bertaux | a webbased service of atmospheric transmission computation for astronomy[END_REF]). Equivalent widths are listed in Table 64. Reported errors are based on the formal one-sigma statistical errors associated to the fit. Representative examples of fits for each DIB are presented in Fig. 68.

NIR-visible comparisons

We used the subset of APOGEE targets with high-resolution optical spectra to study various relations. In addition to the APOGEE+NARVAL/SOPHIE data, we included the results by Cox et al. (2014). Compared to their results, our targets probe smaller column material, but the number of targets is now strongly increased, namely from 9 to 58 lines of sight. Our goal is to study how the λ15 273 DIB compares with these optical bands, especially those that are the most or the least sensitive to the line-of-sight type.

We performed ODR linear fits for the five optical DIBs λλ5780, 5797, 6196, 6283, and 6614. Results presented in Fig. 69 show that the strongest infrared DIB (i.e., the DIB at 15 273 Å) is well related with the strongest optical DIBs, which in turn trace the amount of interstellar matter along a line of sight well (e.g., [START_REF] Merrill | Unidentified Interstellar Lines // PASP[END_REF]Herbig 1993b). This supports the use of this DIB as a tracer of the extinction, for instance. This is of particular interest to map the extinction along very reddened lines of sight that are impregnable at optical wavelengths. The corresponding Pearson correlation coefficients r and reduced χ 2 s are shown in the Fig. 69. All correlation coefficients are above 0.81, that is, they are similar to the average coefficients for the optical DIBs (Friedman et al. 2011). Interestingly, the best reduced chi-squared of the five DIBs is found for the λ6283 band, and the worst correlation is found for the λ5797 band, with a variation by a factor of almost 2 between the two bands, which is quite significant. For all DIBs observed by Cox et al. (2014), HD147889 is the most spectacular outlier, followed by HD161056. Each panel contains a main graphic on top, with the observed spectrum in black and the fit in red. Integration limits for estimating the equivalent width are marked in cyan. The auxiliary graphic at the bottom contains the residuals in red.

6. Weak Near-infrared diffuse interstellar bands 6.5.4 Influence of the environment on the λ15 273 DIB: detection of an edge effect for the λ15 273 DIB

It is well known that the relative strength of optical DIBs varies with the line of sight (e.g., [START_REF] Krelowski | Spatial correlation between CH, CN and the diffuse interstellar band carriers[END_REF][START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Cox, Spaans 2006;Friedman et al. 2011;Vos et al. 2011;Cordiner et al. 2013), which reflects the reaction of the DIB carriers to the properties of the ISM. On the one hand, the so-called σ-type clouds, named after σ Sco, are associated with lowdensity clouds and/or locations that are exposed to interstellar UV radiation field. On the other hand, ζ-type clouds, named after ζ Oph, are associated with higher densities areas that are better protected from the UV radiation. Specifically, [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF] found that the λ5780 DIB carrier is more abundant in the edge of the clouds (i.e., a σ-type unshielded location), while that of λ5797 DIB struggled to survive at these locations and instead reached its maximum in the shielded core of the clouds. Thus the ratio between the strengths of the λλ5797 and 5780 DIBs is well suited to distinguishing these two types of sight lines, and it has been used as a means to quantify the exposure to the UV radiation (e.g., Maíz Apellániz et al. 2015;Cordiner et al. 2013): sight lines with low EW(λ5797)/EW(λ5780) values are classified as σtype, while those with high EW(λ5797)/EW(λ5780) are considered as ζ-type.

Different limiting values to separate between these two types of clouds can be found in the literature. As a reference, we use in our discussion a ratio of EW(λ5797)/EW(λ5780) ≃ 0.32, as proposed by Vos et al. (2011).

Because only few lines of sight have measurements on both the λ15 273 infrared DIB and the λ5780 and λ5797 optical DIBs, the reaction of the λ15 273 infrared DIB to the UV radiation field has not been addressed until now, as far as we are aware. Our sample of about 60 lines of sight is large enough to allow us to do so. Since we lack color excess determinations, we cannot use normalized equivalent widths (i.e., EW/E(B-V) ratios), as has been done by [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF], for example. Instead, we make use of a series of optical bands that are known for reacting to the radiation environment in a different manner (from blue to red: λλ5780, 5797, 6196, 6283, and 6614), and we test the sensitivity of the λ15 273 band to the environment by comparing its strength with the one of each of these bands for all our targets. Individual 5797/5780 ratios for each target are used as a quantitative measurement of the radiation. In this way, identifying which of the λ15 273/optical DIB ratios appears independent of the EW(λ5797)/EW(λ5780) ratio allows us to associate the behavior of the infrared DIB with that of this optical DIB, which in turn places constraints on its carrier and assesses its diagnostic potential.

The measured ratios are ordered according to their degree of variability with respect to the EW(λ5797)/EW(λ5780), as presented in Fig. 610. In general, differences between ratios are much larger in the σ-type regime (unshielded) than in the ζ-type regime, where the behavior is smoother even though dif-6.6. Conclusion ferences still exist. The strongest variation is found for the ratio involving the λ5797 DIB, which is weakened in the presence of a strong UV radiation field [START_REF] Ehrenfreund | The Environment Dependence of Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF][START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]. Conversely, the λ15 273 infrared DIB follows the λ5780 band more closely, and even more so the λ6283 band. The comparison with the two other optical DIBs under consideration (λλ6196 and 6614) displays an intermediate behavior. Our results therefore point toward a connection between the carriers of λλ5780 and 6283 DIBs, and that for the λ15 273 infrared DIB. For example, we might expect an ionization potential for the carrier of the λ15 273 infrared DIB smaller than 13.6 eV, as proposed for the λλ5780 and 6283 DIBs [START_REF] Ehrenfreund | The Environment Dependence of Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF].

Our results for the comparisons with the λ5780 and λ5797 optical DIBs are similar to those obtained by [START_REF] Hamano | Near-infrared Diffuse Interstellar Bands in 0[END_REF][START_REF] Hamano | Near Infrared Diffuse Interstellar Bands Toward the Cygnus OB2 Association[END_REF] for the 10780, 19792, 11797, 12623, and 13175 Å bands: the NIR DIB is better correlated with the λ5780 band. According to these authors, the tight correlations with this band, which is favored in a strong UV field, support the idea that the carriers for the six DIBs are cation molecules. An in-depth discussion of the nature of the λ15 273 DIB carrier is not possible at this stage, but we would like to highlight here that because both the λλ5780 and 6283 DIBs are enhanced in presence of strong radiation fields [START_REF] Ehrenfreund | The Environment Dependence of Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF][START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Vos et al. 2011), our results support the possibility of using the λ15 273 DIB in a similar way. In other words, this DIB may be a good tool to be used as a proxy of the environmental properties, especially in highly reddened areas.

We emphasize that in the context of 3D ISM mapping the skin effect is a second-order phenomenon. Clouds are assigned a distance by means of positive DIB EW radial gradients, and EW radial gradients are positive at cloud crossings regardless of the amplitude of the skin effect. On the other hand, the enhancement of the λ15 273 DIB in external layers of clouds exposed to the radiation may prevent an optimal localization of the cloud core, and may instead spread the reconstructed cloud core in a wider volume compared to its actual one. However, given the poor spatial resolution reached by current 3D maps, this is not important. Conversely, future high-resolution and high-quality measurements may take advantage of the skin effect and use the DIB ratios to construct more detailed maps and simultaneously detect the environmental effects.

Conclusion

In this contribution, we presented an in-depth exploitation of the TSS spectra of the Apache Point Observatory Galactic Evolution Experiment (APOGEE), as provided by the SDSS DR12. The work follows the path opened by (Chapter 2014) targets, the OHP-SOPHIE and TBL-NARVAL targets correspond to shorter sight line and weaker absorptions. For the λ6283 DIB only the latter two datasets are presented. Correlation coefficients r and reduced χ2 resulting from the linear ODR fit are indicated in each plot.

2; Elyajouri et al. 2016) and makes extensive use of the catalog presented there for the IR DIB at λ15 273. In addition to the IR data, we make use of high-resolution optical spectra obtained with SOPHIE and NARVAL. 5780). The right scale range is chosen from zero to three times the average ratio. An ODR linear fit provides the relative slopes indicated in the figure along with the correlation coefficients. The largest slope is found for λ5797 band and the smallest for the λ6283 DIB. Here, we show (red line) an exponential fit to the whole dataset and a linear fit (black dashed line).

The main results and conclusions of this work can be summarized as follows.

1. We provide a catalog of measurements of the strength (as traced by 133 6. Weak Near-infrared diffuse interstellar bands the equivalent width) and central wavelength for λλ15617, 15653, and 15673 DIBs with a total number of 295, 262, and 308 detections, respectively. This constitutes the largest compilation of measurements for these DIBs to date.

2. We made use of this large number of detections to characterize in detail the central wavelength, width, and shape of these three DIBs. All of them have a FWHM>2 Å. The estimated upper limit for the intrinsic widths are 4.4, 5.7, and 3.7 Å for the λλ15617, 15653, and 15673 DIB, respectively. We explored the shape of the DIBs by creating a spectrum of extremely high S/N ratio through stacking. All the three bands seem asymmetric and have a shallower slope in the red wing, similar to what is observed in most optical DIBs. The asymmetry is stronger for the λ15617 band. We used the stacked spectrum to derive an average FWHM of 3.9 Å for the λ15 273 band. This value is slightly lower than the distribution peak value of 5.1 from Zasowski et al. (2015).

3. We searched for weaker previously reported IR DIBs. To do so, we stacked spectra since the S/N is not good enough to extract measurements for these DIBs in an individual sight line. We confirm the previously reported detection of DIBs at λλ15990, 16232, 16573, and 16585. We do not find any absorption feature at λ16596, nor at λ15225, where DIB candidates have previously been reported [START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF].

Our in-depth search suggests a possible existence of two additional DIBs at λλ15235 and 16769.

4. We provide first average ratios for the four NIR DIBs λλ15 273, 15617, 15653, and 15673.

5. We used a total of about 60 spectra to explore the relation between the strongest infrared DIB (λ15 273) and several strong optical DIBs.

The IR DIB correlates well with all of them, with Pearson coefficients always higher than 0.8. We fit a linear regression to all the pairs IR DIB -optical DIBs. The best χ 2 is found for the pair involving the DIB at λ6283, pointing toward a close relationship between the carriers of these two DIBs.

6. This relationship is confirmed when we explore the behavior of the λ15 273 DIB with respect to the environment. The IR DIB nicely follows the λ5780 band, similarly to the 10780, 19792, 11797, 12623, and 13175 Å bands (Hamano et al. 2016), and it even more tightly follows the band at λ6283. It therefore probably forms in similar environmental conditions (i.e., in a relatively strong UV radiation field). We propose the λ15 273 DIB (or a ratio involving this DIB) as an infrared diagnostic of the physical conditions of the ISM.

A feature in the near-IR with this capability is particularly relevant since it constitutes a tool able to trace the environmental conditions 6.6. Conclusion in lines of sight that are impenetrable to optical wavelengths. Moreover, we are living in an epoch where astronomy is becoming more and more infrared oriented. For example, the advent of space multiobject spectrographs such as JWST's NIRSPEC will provide massive amounts of NIR "free-telluric" data and highly multiplexed infrared spectrographs for 10 m telescopes like MOONS (Cirasuolo et al. 2014) will be soon in operation. Likewise, high-resolution spectroscopy with the ELT family will also prioritize the infrared.

Even if our interest is in lines of sight that are transparent enough in the optical, it is therefore desirable to develop equivalent diagnostics at near-IR wavelengths. 135 6. Weak Near-infrared diffuse interstellar bands 7.2. Observations and data reduction clouds, it is also possible to map the evolution of DIB carriers with extinction from the edge (representing diffuse ISM) to the core (representing dense ISM) of a single dark cloud.

(m Å) (m Å) (m Å) (m Å) (m Å)
λ c 15653 λ c 15673 Vrad (deg) (deg) (m Å) (m Å) (m Å) ( Å) ( Å) ( Å) (km.s -
Tracing the behaviour of DIBs for A V up to 10 mag is, at present, only practical when using strong DIBs in the NIR. The 15 273 Å DIB is particularly suited as its presence and properties in the ISM have been studied extensively thanks to the inclusion of this band in the APOGEE NIR survey (Zasowski et al. 2015) and as seen in Part I and also thanks to the strength of this DIB.

Here we present KMOS spectroscopic observations of the 15 273 Å DIB toward 43 background sources probing Barnard 68 (B68). B68 is a nearby (125 pc), relatively isolated, starless Bok globule conveniently situated against a backdrop of Galactic bulge (giant) stars as illustrated in Fig. 71. This globule is often seen as the prototypical case of a Bonner-Ebert sphere and shows a radial averaged extinction law, spanning from a few to several tens of magnitudes in visual extinction, which follows the predicted case very closely (Alves et al. 2001). The radial density and temperature profile of B68 have been reconstructed accurately through NIR extinction and far-infrared and sub-mm emission measurements (e.g., [START_REF] Bergin | The Thermal Structure of Gas in Prestellar Cores: A Case Study of Barnard 68[END_REF][START_REF] Nielbock | The Earliest Phases of Star formation (EPoS) observed with Herschel: the dust temperature and density distributions of B68[END_REF][START_REF] Steinacker | Prestellar core modeling in the presence of a filament. The dense heart of L1689B // A&A. VIII 2016. 593. A6. Bibliography Steinicke W. William Herschel's 'Hole in the Sky' and the discovery of dark nebulae[END_REF]).

Observations and data reduction

The KMOS [START_REF] Sharples | First Light for the KMOS Multi-Object Integral-Field Spectrometer // The Messenger[END_REF]) H-band spectra of 85 targets toward B68 were obtained in February and March 2016 (Program ID 096.C-0931(A), PI: N. Cox). The targets are located within ∼200 ′′ from the center of the cloud. The 43 targets whose spectra could be used in the analysis (Sect. 7.3) are shown in Fig. 72. The initial target list was based on selecting the 2MASS targets (J ∼ 12 -16 mag) at a range of distances from the center of the cloud. The final target selection was governed primarily by finding the most efficient configuration of the KMOS arms for the allocated time. Four instrument setups were defined and executed. Two configurations, in stare mode, were used to obtain spectra of the brightest target (J = 12-14 mag) and two for the faintest targets (J = 14-16 mag). The sky positions were observed every three science exposures. The parameters, seeing, and airmass are listed in Table 71. For each configuration about 20 arms were assigned to science targets and 3 to 4 arms were used for sky observations. The data were reduced via the KMOS data processing workflow [START_REF] Davies | The Software Package for Astronomical Reductions with KMOS: SPARK // A&A[END_REF] within the ESO Reflex environment. Average spectra were extracted from each 2.8 ′′ × 2.8 ′′ IFU. In most cases optimal extraction was chosen. In two instances two sources were captured by the IFU and these were extracted separately using average extraction from specified spatial windows.

Extraction of the interstellar spectrum

tracted from the Apache Point Observatory Galactic Evolution Experiment (APOGEE; Majewski (2012)) Stellar Parameters and Chemical Abundances Pipeline (ASPCAP; García Pérez et al. 2016b), which provides a synthetic stellar spectrum estimating the main stellar line locations for each giant star.

In particular, we used models from the GK grid, which covers 3500-6000 K (Mészáros et al. 2012). From this set a sample of 1500 ASPCAP models, with T eff = 3634 -6000 K, has been selected randomly. Since each APOGEE model is given in the corresponding stellar rest frame, the fitting procedure allows for a wavelength shift in order to compensate for the star radial velocity. Telluric transmission models of H 2 O adapted to the observing site, date, and zenith angle were retrieved online from the Transmissions of the AtmosPhere for AStromomical data (TAPAS) service [START_REF] Bertaux | a webbased service of atmospheric transmission computation for astronomy[END_REF]).

The stellar and telluric spectra were convolved with a Gaussian instrumental response for R=4000. Fig. 73 shows an example of stellar spectrum and a typical telluric transmission before and after convolution. More fits are presented in appendix B.

Fitting method

Each individual spectrum was fitted by a combination of the convolved synthetic stellar spectrum S λ and the convolved telluric transmission model T λ with a 8 Å mask applied at the expected location of the DIB (around 15271 Å). The ill-fit stellar lines regions have also been masked. A linear function (parametrized with A and B) is included to adjust to the continuum level. Our model is described as follows :

M λ = (S λ (δλ)) α × T β λ × (A × λ + B)
, where α and β are the scaling factors that adjust the stellar and telluric feature depths to the observed flux and δλ is the free wavelength shift of the model. The mathematically correct formula is the convolved product instead of our product of preliminarily convolved functions. However, we checked that our simplified formula provides very similar fit results, owing to the broad instrumental function, but is much more efficient. As we focus on the strongest NIR DIB 15 273, we selected a predefined fraction of the spectral range around the DIB location, i.e., 15216-15424 Å. All parameters A, B, α, β, and δλ were free to vary. The fit starts with the same initial guess except for the telluric scaling factor, which is chosen according to the date of observation. We performed the adjustment for each of the 1500 ASPCAP models for each target and retained the model with the minimum χ 2 . We require the mean standard deviation to be less than 2.6% outside the DIB, which yields a sample of 43 targets with clean and well-enough fitted spectra. A visual inspection of the residuals (R λ = observed flux -total best-fitting model) allowed us to confirm the reliability of the procedure and that for selected targets the remaining fluctuations are solely due to the noise. These residuals were used to derive the actual noise level and error bars in individual spectra and stacked spectra (see below). The S/Ns in individual spectra range from 39 (i.e., our threshold) to ≃125. Our method is illustrated in Fig. 73. To check the feasibility of the DIB extraction we then performed a simulation using a single spectrum and its fitted model. We added DIB absorptions with various strengths corresponding to the range of extinctions encountered in B68 and following the Zasowski et al. (2015) relation EW = 102 m Å × A (1.01±0.01) V derived over three orders of magnitude in extinction. Fig. 74 shows that, in case the average law is verified, DIBs can be detected in this individual spectrum provided A V ≥ 2.

15 273 Å DIB characterization

The residuals provide a first estimate of DIB detection or non-detection. However, we found that the DIB depth is often on the order of the noise amplitude. In order to improve the characterization of the DIB, we stacked residuals within groups of stars according to their A V s in five intervals centered on {2.1; 5.2; 9.2; 13; 19.8} as shown in Fig. 74. The corresponding numbers of targets in each group are {19; 9; 9; 3; 3} and the S/Ns decrease1 from ≃170 to ≃75. Then, we performed two Gaussian fits at the DIB location. We have shown in Chapter 6 that a Gaussian profile is appropriate. The first fit is carried out for a fixed DIB width and location, as if the DIB feature was only due to the dark cloud. In the second, both width and locations were free to vary. Uncertainties on EWs are estimated conservatively from the noise level as in Chapter 2. Fig. 75 shows the measured EW as a function of A V toward the B68 background star groups. The blue points show EWs with all free fit parameters and the red points correspond to the single cloud case. The results are compatible within error bars, but the fixed width case is found to correspond to a smoother evolution than the free width case, suggesting that this method is more reliable and from now we adopt this solution. The results are compared with expectations from the above galactic average of Zasowski et al. (2015). It can be seen that the measured reddening values at cloud periphery and center would imply 2.7 and 6.8 times higher values.

Simplified inversion

We performed a simplified inversion of DIB carrier columns (here EWs scaled by an unknown fixed number) to retrieve the true radial (3D) structure from the projected (2D) observed structure. We assumed that B68 is spherical and composed of concentric homogeneous spherical shells and 7. Tomography of single cloud pc. The resulting volume densities n(H) range from 0.5 to 3.5 ×10 5 cm -3 (Fig. 75) and are roughly consistent with the results of [START_REF] Nielbock | The Earliest Phases of Star formation (EPoS) observed with Herschel: the dust temperature and density distributions of B68[END_REF], showing that despite the limited number of shells the procedure gives reasonable estimates of volume densities. Fig. 75 also shows that the fractional abundance n(DIB)/n(H) decreases by a factor ≃ 3 from periphery to center. A simple scaling shows that n(DIB)/n(H) is 2.7 (resp. 7.9) smaller than the average deduced from the Zasowski et al. (2015) law at 0.018 (resp. 0.059) pc from cloud center.

Discussion

VLT KMOS spectra of stars beyond B68 were used to derive the strengths of the associated 15 273 Å DIB and to infer a first estimate of the volume density distribution of the DIB carrier. Following the general trend established for most optical DIBs (see, e.g., (e.g., Lan et al. 2015), the DIB EW levels off with respect to the extinction. At A V =2.1 it is already at least 2.7 times below the average level measured along distant sightlines (Zasowski et al. 2015), and for our most central direction with A V ≃20, it is ≃7 times below this level. In terms of volume densities in the spherical approximation, this corresponds to a 2.7 (resp. 7.9) depletion from periphery (r=0.018 pc) to center (r=0.059 pc). This 15 273 Å DIB depletion is less severe here than for strong optical DIBs in the remarkable case of the Orion star HD 62542 [START_REF] Snow | Unusually Weak Diffuse Interstellar Bands toward HD 62542[END_REF], in particular in the case of the 6284 Å DIB, which is at least 25 times depleted with respect to average shielded ζ-type sightlines ( Ádámkovics et al. 2005). This may be surprising since both DIBs have similar average responses to the radiation field as seen in the Chapter 6. Fig. 75 compares the dependence on the extinction of the 15 273 Å in B68 and of the 6284 Å DIB in Fig. 9 of Lan et al. (2015). A scaling factor was applied to match the strength of DIB 15 273 Å in the linear region. There is a marked leveling off with respect to the extinction at A V ≃ 1 for both DIBs. Above A V =2 the EW decrease observed for the 6284 Å DIB may be absent for the 15 273 Å DIB, however the overlap in the extinction ranges is too small to reach a firm conclusion. If confirmed, such differences would show that, despite the similarity between the responses to the radiation field, the 15 273 DIB carriers do not behave in the dark cloud B68 as the 6284 DIB carriers in molecular clouds or Orion nebula stripped clouds. The life cycle of the carriers and its drivers are still under study. The UV radiation is recognized to play a primary role and in the context of PAH-like carriers it would certainly directly influence the charge state of PAHs (e.g., (e.g., [START_REF] Ruiterkamp | PAH charge state distribution and DIB carriers: Implications from the line of sight toward ¡ASTROBJ¿HD 147889¡/ASTROBJ¿[END_REF]) and the resulting disappearance of some DIBs. Additional photo-processing and hydrogenation or dehydrogenation likely enter into play (e.g., [START_REF] Vuong | Dehydrogenation of polycyclic aromatic hydrocarbons in the diffuse interstellar medium[END_REF]. [START_REF] Bertaux | Diffuse interstellar bands carriers and cometary organic material ?[END_REF] have suggested that a general carrier disappearance could additionally be due to coagulation into aggregates, as predicted by some evo-7.5. Discussion lutionary models of dust [START_REF] Jones | Dust evolution, a global view: II. Top-down branching, nanoparticle fragmentation and the mystery of the diffuse interstellar band carriers[END_REF]. Our study demonstrates that future measurements using multiplex NIR spectrographs and extended to more DIBs, more targets, and various types of clouds should allow one to probe the volume density distribution of the DIB carriers precisely enough to shed light on the processes that govern their carrier formation and destruction. However, the case of Barnard 68 is still comparable to tens of other similar cases seen in Chapter 5, suggesting that the marked disappearance of DIBs in Barnard 68 is far from an exception.

Chapter 8

Description of the EDIBLES survey

Overview

The ESO diffuse interstellar band large exploration survey (EDIBLES) is a Large Programme that is probing the lines-of-sight of ∼120 bright O and B-type stars over a large spectral range with UVES (Smoker et al. 2009). EDIBLES focuses on the links between the various detectable DIBs and the properties of the diffuse to translucent ISM located in front of the selected targets. The survey is designed to cover different physical and chemical environments.

EDIBLES observations and data reductions are described in detail in [START_REF] Cox | The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) . I. Project description[END_REF] listed below and illustrated in Fig. 9 of this article. The S/N values for most sightlines are 400-1000 in the red part, 300-400 in the UV and ≥ 300 in the near-IR. Two additional data treatments are necessary to realize the goals aimed for in this study.

First, we have corrected all individual exposures recorded with the 564-nm setting (including both the Red Upper and Red Lower detectors) for their telluric lines. The method follows the first of the two procedures described in [START_REF] Cox | The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) . I. Project description[END_REF]. It is appropriate for moderately weak lines and uses TAPAS synthetic spectra adapted to the observing site and season [START_REF] Bertaux | a webbased service of atmospheric transmission computation for astronomy[END_REF]). In addition to the telluric correction of the entire spectra, we have used the same method to remove the weak telluric lines that contaminate the spectral region comprising the C 2 Phillips system. During the correction process, we noticed an additional system of narrow telluric lines in the 5780-5815 Å region that are lacking in the present TAPAS model. These very weak lines have been added very recently to the HITRAN database and correspond to the b 1 Σ + g (v=3)-X 3 Σ - g (v=0) band of O 2 [START_REF] Gordon | The HITRAN2016 molecular spectroscopic database[END_REF]). Pending their introduction into TAPAS, we have used a weakly reddened star and extracted the system of lines in this spectral area. We have used this empirical transmittance in the same way as the TAPAS one. At the time

Introduction

The unknown identity of the carriers of all but two diffuse interstellar bands (DIBs) constitutes the longest standing spectroscopic enigma of modern astronomy (Sarre 2006). Two features at 5797 and 5780 Å, which are now known to be interstellar in origin, were first noted by Heger (1922) and studied in relation to interstellar gas and dust by Merrill & Wilson (1938). At present, over 400 of these interstellar absorption features are known (for a handful of sightlines), superimposed on an otherwise nearly smooth interstellar extinction curve (Herbig 1995;Galazutdinov et al. 2000;Hobbs et al. 2008). Only recently has the attribution of a pair of near-infrared DIBs (Foing & Ehrenfreund 1994) to C + 60 been confirmed with laboratory gas phase experiments (Campbell et al. 2015;Kuhn et al. 2016) along with the tentative astronomical detection of three more predicted bands (Walker et al. 2015(Walker et al. , 2016)), though this needs further verification and investigation (Galazutdinov et al. 2017;Cordiner et al. 2017). This is an exciting result because C 60 (Cami et al. 2010;Sellgren et al. 2010), and C + 60 (Berné et al. 2013) have also recently been detected in space through their mid-infrared emission spectra. This identification may be a chemical clue towards identifying further DIB carriers; so far only C 3 (Haffner & Meyer 1995;Maier et al. 2001;Schmidt et al. 2014) and C + 60 have been identified as pure polyatomic carbon species in the diffuse ISM and this leaves a large gap to be filled in our current understanding of the carbon chemical network in diffuse clouds. It is possible that the detection of C + 60 hints at a long predicted important role of polycyclic aromatic hydrocarbons (PAHs) in the ISM ( Van der Zwet & Allamandola 1985;Léger & d'Hendecourt 1985;Salama et al. 1996). Recent laboratory (Zhen et al. 2014) and modelling (Berné et al. 2015) works support the proposal by [START_REF] Berné | Formation of buckminsterfullerene (C60) in interstellar space[END_REF] that fullerenes may form upon photo-dissociation of large PAH precursors.

Observational surveys (e.g. Herbig 1993;Friedman et al. 2011;Kos & Zwitter 2013) have shown that the strength of the strongest ∼20 of the DIBs correlates roughly linearly with the amount of dust and gas, measured by the reddening E(B -V) or the column density of atomic hydrogen N(H i), respectively. This indicates a thorough mixing of the DIB carriers with Article published by EDP Sciences A76, page 1 of 29 A&A 606, A76 (2017) interstellar matter (Cox 2011). However, a large real scatter is observed in these relations with gas and dust, and the relative strengths of several bands are known to have an environmental dependence (Cox & Spaans 2006); some bands vary as a function of radiation strength between different lines-of-sight (Krełowski & Walker 1987;[START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Vos et al. 2011;Friedman et al. 2011). The scatter is also partly due to multiple cloud structures along sightlines. The relationship between several DIBs and, for example, C 2 and CN, has been investigated (Thorburn et al. 2003;Weselak et al. 2008), but generally the link with di-atomic species is not well understood. This relation between DIBs and reddening has not been investigated for the remaining >380 bands. Whether or not there is a direct physical connection between DIB carriers and dust grains, e.g. in terms of depletion onto grains or as carrier formation sites, remains to be seen -so far no polarisation signal has been detected for the twenty strongest DIBs (Cox et al. 2011).

Studies of selected bands in a dozen sightlines have revealed a complex substructure in the narrowest bands (Sarre et al. 1995;Ehrenfreund & Foing 1996;Galazutdinov et al. 2008) which show small variations with local temperature (Cami et al. 2004;Kaźmierczak et al. 2010a), typical for a molecular carrier. Substructure has also been identified in weak DIBs, but line-of-sight variations are less well studied (Galazutdinov et al. 2005). On the other hand, broader DIBs do not contain substructure (Snow 2002;Galazutdinov et al. 2008), which may be due to lifetime broadening of the absorption band (Linnartz et al. 2010). Considerations of the available elemental abundances and plausible oscillator strengths lead to the conclusion that abundant large organic molecules are suitable candidates (Léger & d'Hendecourt 1985;[START_REF] Huang | Constraining the size of the carrier of the λ5797.1 diffuse interstellar band[END_REF]. The combination of observational studies, theoretical models, and laboratory astrophysics indicates that candidate carriers should primarily be sought among a large number of possible carbon-based organic molecules (see Sarre 2006, for a review;and Cami & Cox 2014, for an overview of recent progress).

Ongoing and future large spectroscopic surveys offer the possibility to study (mostly the strongest) DIBs in large areas of the sky. For example, Lan et al. (2015) and Baron et al. (2015) constructed DIB strength maps from SDSS spectra, Kos et al. (2014) produced pseudo-3D maps for the 8621 Å DIB using the RAVE survey, Zasowski et al. (2015) and Elyajouri et al. (2016) used the APOGEE near-infrared survey to study the distribution of the 15 267 Å near-infrared DIB. The spatial distribution and properties of DIBs can also be studied in smaller fields-of-view (van Loon et al. 2009;Raimond et al. 2012;Puspitarini et al. 2015) or closer regions, such as the Local Bubble (Farhang et al. 2015;Bailey et al. 2016).

In the last decade it has also been firmly established that many band carriers are universal; DIBs have been detected and surveyed in the Magellanic Clouds (Cox et al. 2006(Cox et al. , 2007;;Welty et al. 2006;van Loon et al. 2013;Bailey et al. 2015), in M 31 and M 33 (Cordiner et al. 2008(Cordiner et al. , 2011)), and in individual sightlines in more distant galaxies (Junkkarinen et al. 2004;Sollerman et al. 2005;Lawton et al. 2008;Cox & Patat 2008, 2014;Monreal-Ibero et al. 2015). DIB carriers therefore constitute an important reservoir of (organic) material throughout the Universe.

Identifying the DIB carriers and understanding their properties must come from high-quality data in the nearby Galactic interstellar medium (ISM). Identification of the carrier species will directly impact our understanding of interstellar chemistry, and can help reconstruct 3D line-of-sight properties if related to specific environments. It is clear that the ultimate confirmation must come from a direct comparison between astronomical, theoretical, and laboratory spectra over a broad wavelength range. A commonly applied and straightforward approach is to acquire laboratory spectra of possible candidate carriers taken under astrophysical relevant conditions until an unambiguous match with the astronomical data is found. With the notable exception of the above mentioned work on C + 60 previous studies have thus far failed, such as attempts to link PAH cations (Bréchignac & Pino 1999;Salama et al. 2011Salama et al. , 1999;;Romanini et al. 1999), neutral PAHs (Salama et al. 2011;Gredel et al. 2011), carbon chains (Motylewski et al. 2000;Maier et al. 2004), or H 2 (Sorokin & Glownia 1999;[START_REF] Ubachs | IAU Symp. 297[END_REF]) to the DIBs. The search for a laboratory match can be optimised if the most likely candidates can be pre-selected out of the vast collection of possible species, and if the relevant conditions can be accurately constrained. Hence it is necessary to unravel the physical and chemical properties of the DIB carriers through analysis and modelling of observations. This includes deriving environmental conditions that affect their strength/profile shapes, as well as understanding the molecular physics and spectroscopy of candidate carriers.

This paper presents the observational overview of the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) and how we intend to use the obtained spectra in our long-term goal of reverse-engineering the molecular characteristics of DIB carriers. In Sect. 2 we describe the scientific goals and immediate objectives of EDIBLES. Section 3 describes the methodology and survey design. The survey target selection is discussed in Sect. 4 and the data processing steps are described in Sect. 5. Section 6 discusses several confounding factors such as telluric and stellar spectral lines. In Sect. 7 we present a preview of the EDIBLES data and illustrate their scope and quality. A brief summary is given in Sect. 8.

Scientific goals and immediate objectives

The primary science goal of EDIBLES is to reverse-engineer molecular characteristics of DIB carriers, through studying the behaviour of DIBs in relation to the physical and chemical parameters of their environment. This approach differs from earlier work in which attempts to identify DIBs were based mainly on direct comparisons of astronomical and laboratory or theoretical spectra. A large systematic high-fidelity survey of the diffuse-totranslucent ISM is necessary to realise this approach.

The aim is to assemble a sample of interstellar spectra with sufficiently high spectral resolution and signal-to-noise ratio to allow detailed analysis of numerous DIBs and known atomic and molecular absorption lines in the same lines-of-sight. At the same time, our sample is designed to sample a wide range of interstellar conditions, in terms of reddening, molecular content and radiation field, within a practical observing time.

With EDIBLES we plan to compile the global properties of a large ensemble of both weak and strong DIBs, and variations therein, as a function of depletion (patterns) and local physical conditions. The new dataset should allow us to: (a) determine the relation between weak and strong DIBs by identifying correlations and sequences; (b) identify (sets of) DIBs that correlate with different physical conditions in the ISM, and assess whether the DIBs can be used to determine those conditions as a remote diagnostic tool; (c) study the physico-chemical parameters that influence the DIB properties, by using state-of-the-art chemical modelling, combined with extensive auxiliary line-of-sight data (e.g. on dust); and (d) constrain the chemical composition of the A76, page 2 of 29 N. L. J. Cox et al.: The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES). I.

DIB carriers by studying their relation to interstellar elemental abundances (depletion levels) and dust grain properties and composition derived from, for example, the UV-visual extinction (including the conspicuous 2175 Å UV bump) and optical polarisation curves.

A number of studies have attempted to investigate links between the physical and chemical conditions of the ISM and the properties of the DIBs. However, most studies focus only on a few strong bands in a moderate-to-large number (≈100) of sightlines (Friedman et al. 2011;Vos et al. 2011;Zasowski et al. 2015), or on many DIBs in just a few sightlines [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]Tuairisg et al. 2000;Hobbs et al. 2008Hobbs et al. , 2009)). Hence, more recent progress in the field has been limited to the study of only a handful of the strongest DIBs due to high demands on the signal-to noise ratio (S/N), spectral resolution, the removal of stellar and telluric lines, and the lack of large, uniform data sets. EDIBLES is designed to fill this gap, making just such a large, uniform data set available and thus enabling a large and systematic study of the physical and chemical parameters that are expected to directly influence the formation efficiency and spectroscopic response of DIB carriers.

Methodology and survey design

EDIBLES provides the community with optical (∼305-1042 nm) spectra at high spectral resolution (R ∼ 70 000 in the blue arm and 100 000 in the red arm) and high signal-to-noise (S/N; median value ∼ 500-1000), for a statistically significant sample of interstellar sightlines. Many of the >100 sightlines included in the survey already have auxiliary available ultraviolet, infrared and/or polarisation data on the dust and gas components.

Studies of DIBs typically report data such as equivalent width, central depth, profile shape and substructure identification. These cannot easily be compared between surveys due to differences in the instrumentation, data quality and analysis procedures -e.g. continuum normalisation and measurement of spectroscopic lines. Archival material comprises a heterogeneous sample of spectra with varying S/N, resolving power, and spectral coverage. To achieve the goals and objectives described above requires a large and homogeneous survey of UV/visible spectroscopic tracers across a broad spectral range, covering a broad variety of interstellar environments. From this selfconsistent set of observations we can extract:

1. Accurate column density measurements (or upper limits) for the most important atomic and molecular species, across a wide spectral range. These can be used to assess the velocity structure of the line-of-sight (in particular to determine radial velocity differences between species; e.g. Bondar et al. 2007), derive depletion levels of metals, infer and compute physical conditions (within the limitations imposed by the current knowledge on interstellar processes), using diffuse cloud PDR models (Le Petit et al. 2006) or turbulent energy dissipation models (cf. Flower & Pineau des Forêts 2015; Godard et al. 2014;Bron 2014).

For the photo-chemistry and derivation of particle density, radiation fields, turbulent energy dissipation, the key transitions are those of CN λλ3874, 7906, CH λλ3879, 4300, and CH + λλ3958, 4232 Å, together with H 2 (from archival UV spectroscopy). For example, rotational temperatures can be derived from bands of C 2 (λ8756) and C 3 (λ4053), and cosmic ray ionisation rates can be derived from OH + abundances (λλ3300-3600 Å). CH measurements can be used to estimate the H 2 column density (Danks et al. 1984;Weselak et al. 2004). 2. Accurate measurements of DIB profiles (asymmetries, wings, substructure) and variations therein. Substructure can be related to molecular properties/sizes of carrier species (Kerr et al. 1996;Ehrenfreund & Foing 1996;[START_REF] Huang | Constraining the size of the carrier of the λ5797.1 diffuse interstellar band[END_REF] with variations due to changes in the rotational temperature (Cami et al. 2004;Kaźmierczak et al. 2010b) or the presence of hot bands (Marshall et al. 2015). 3. Updated measurements of peak positions of weak (per unit reddening) diffuse bands along single cloud sightlines. 4. Measurements and cross-correlation of over 50 weak and strong bands along the most reddened sightlines (E(B -V) > 0.4 mag). Correlations between strong and weak bands might reveal additional information on groups (or families) of DIBs, but it should be noted that a strong correlation between DIBs is not a necessarily a guarantee that they have a common carrier (McCall et al. 2010;Krełowski et al. 2016). 5. Stacking analyses to search for molecules and/or DIBs which are too weak to be seen in individual spectra. 6. Firm detection limits or abundance constraints on specific molecular carriers for which laboratory spectra are obtained. 7. Variations in interstellar species due to the small-scale structure of the diffuse ISM (Cordiner et al. 2013;Smith et al. 2013).

To achieve our objectives efficiently we use the Ultraviolet Visual Echelle Spectrograph (UVES; [START_REF] Dekker | Optical and IR Telescope Instrumentation and Detectors[END_REF]Smoker et al. 2009) mounted on the 8-m second Unit Telescope (UT2) of the ESO (European Southern Observatory) Very Large Telescope at the Paranal Observatory. The relative brightness of nearby early-type stars allows the observation strategy to take advantage of poor observing conditions and twilight hours that would otherwise be under-utilised. The programme is running as a Large "Filler" Programme (ESO ID 194.C-0833, PI. N. L. J. Cox), which has been allocated 280 h of observing time. About 8500 science exposures with a total exposure time of 229 h (with blue and red arm exposures taken simultaneously) have been collected between September 2014 and May 2017. The program is expected to be completed by late 2017. UVES has two arms, red and blue, which can be used simultaneously by inserting a dichroic mirror [START_REF] Dekker | Optical and IR Telescope Instrumentation and Detectors[END_REF]. To obtain coverage of the entire spectral range accessible with UVES, we use two instrumental settings per target: setting #1: 346+564 and setting #2: 437+860, where the pairs of numbers refer to the central wavelengths in nanometres of the two arms. Together this provides near-continuous wavelength coverage from ∼305 to 1042 nm. The blue and red arm slit widths are 0.4 ′′ and 0.3 ′′ , respectively, yielding nominal resolving powers R = λ/∆λ of ∼71 000 and ∼107 000. Table 1 presents a summary of the instrumental setups. The EDIBLES data presented here were collected over a period of two years, and it is therefore important to realize that the UVES resolution is not fully stable with time 1 , but the values listed in Table 1 were typically realised in the actual spectra.

The "filler"-type observation strategy means that observations are often executed in non-optimal (and unpredictable) conditions e.g. in terms of seeing, cloud coverage, sky emission/air glow, lunar phase, and water vapour content. This needs to be taken into account in the implementation of the observations. Despite these limitations, S/N ratios of 200-300 per exposure can be reached in short exposure times for the bright (2 < V < 6 mag) targets, and up to 20 exposures (to avoid saturation of individual frames) are obtained for each to build up higher S/N. Observations are divided into observing blocks (OBs) for a specific instrument setting and target. OB execution times range from ∼20 min for the bright (V < 6 mag) stars up to ∼45 min for the fainter (6 < V < 9) stars. Additional flat-field calibration exposures were taken during the day-time (when possible, subject to operational constraints) to reduce residual fringing that would persist with the standard UVES calibration plan and to increase the overall S/N ratio. Details of the flat-field corrections are given in Sect. 5.

Target survey sample selection and characteristics

We constructed a statistically representative survey sample that probes a wide range of interstellar environment parameters including reddening E(B -V), visual extinction A V , total-toselective extinction ratio R V , and molecular hydrogen fraction f H 2 . This is essential to (a) trace depletion patterns from diffuse → translucent clouds, (b) study the effect of shock-and photo-processing, (c) probe the behaviour of DIBs with respect to grain properties, and (d) identify unusual DIB environments.

During target selection the following factors were taken into account:

-Given that f H 2 depends non-linearly on A V , due to the transition from atomic to molecular hydrogen driven by H 2 selfshielding, we require numerous sightlines probing A V ∼ 1-3 mag and below, in small increments ∆A V . -The dust grain properties and attenuation of UV photons (important for photo-chemistry) are constrained by the extinction curve, i.e. the A V and R V parametrisation (Valencic et al. 2004;Fitzpatrick & Massa 2007) or from fitting with a well-defined dust-PAH extinction model (Mulas et al. 2013). -Preference is given to sightlines with auxiliary atomic/molecular data, such as H i and H 2 measurements (Jenkins 2009;Gudennavar et al. 2012), optical polarisation data (Whittet et al. 1992;Weitenbeck 2008), or Mg/Fe abundances (Voshchinnikov et al. 2012).

Where two targets with similar interstellar conditions are available, we preferentially selected targets which are brighter and/or of earlier spectral type.

The target list is given in Table A.1 (their Galactic distribution is shown in Fig. 1). Columns ( 1) to (3) provide basic information on the target id (HD number) and coordinates (RA/Dec). Columns (4) and ( 5) list the spectral type and corresponding literature reference. The interstellar line-of-sight dust extinction properties, E(B -V), R V , A V , are given in Cols. ( 6) to (9). Columns ( 10) and ( 11) list the atomic and molecular hydrogen abundances with the molecular fraction f H 2 listed in Col. (12).

The total number of selected targets amounts to 114 (of which 96 have been observed at least once as of May 2017) and comprises mostly bright O and B stars (V ≈ 2-7 mag with a small fraction 7 < V < 9 mag). The sample probes a wide range of interstellar dust extinction properties (E(B -V) ∼ 0-2 mag; R V ∼ 2-6; A V ∼ 0.1-4.5 mag) and molecular content ( f H 2 ∼ 0.0-0.8). The histograms in Fig. 2 illustrate the range of parameters included in the survey sample. In Fig. 3 we show comparisons between visual extinction, A V , and the measured neutral hydrogen column density N(H i), molecular hydrogen column density N(H 2 ), and total hydrogen column density N(H tot ) (=N(H i)+2N(H 2 )). As noted above H 2 can be estimated using the CH transitions (Danks et al. 1984;Weselak et al. 2004).

Data processing

Acquiring high-S/N spectra with UVES is challenging in the context of EDIBLES for a number of reasons. For example, small errors in the wavelength calibration at the edges of individual adjacent orders can cause the appearance of ripples in the continuum in high-S/N exposures. Moreover, the unpredictable seeing and other observing conditions inherent in a filler programme mean that individual exposure times cannot be optimised for the actual sky conditions.

The overall quality of all 8500 science and ∼1600 flat exposures was checked visually. This visual inspection led us to discard about 150 exposures that appeared corrupted. This could, for example, be due to sudden changes in weather conditions or premature termination of exposures. In addition, we carefully inspected for the presence of incorrect thorium-argon lamp, format-check, and flat field exposures that could result in faulty order tracing or wavelength calibration solutions (see also below).

Within a sequence of observations (20-40 min including overheads) the change in barycentric velocity correction is small (<0.1 km s -1 per hour) so the spectra can be averaged without compromising the velocity precision. However, exposures taken on different nights were not averaged. This is to preserve multiepoch information -specifically for spectroscopic binaries and the search for time-variable interstellar absorption -and to avoid addition of misaligned interstellar features due to variations in the barycentric velocity of the frame of the observer.

The data reduction was performed by two semiindependent teams, one using version 5.7.0 of the UVES pipeline (Ballester et al. 2000), esorex (version 3.12; ESO CPL Development Team 2015) integrated in a Python pipeline (hereafter Reduction "A"), and the other using the 4.4.8 version of the UVES pipeline (Reduction "B").

Each set of around 20 science frames was processed with the same set of calibration frames. For the format check, order definition and wavelength calibrations these were the nearest in time, with the master bias and master flats using frames taken typically over several days or weeks. In general, both data reductions used similar parameters for the different pipeline recipes, but with a A76, page 4 of 29 N. L. J. Cox et al.: The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES). I. Order extraction. Various possible extraction methods were tested and we find that the average extraction method with performing a flat fielding before the extraction (see above) leads to a higher S/N with respect to other methods (such as optimal extraction), which is generally the case for high S/N spectroscopy. This method also reduces the fringing seen in the red wavelengths ≥700 nm, although does not entirely remove it. Figure 5 shows that displays parts of an extracted science spectrum near the gap between the upper and lower CCDs. The fringing is much worse in the lower thinned EEV chip than in the upper MIT thick chip (Smoker et al. 2009).

Cosmic ray rejection and hot pixels. To remove the cosmic rays and possible hot pixels we applied a sigma-clipping method to each extracted spectrum. The default sigma-clipping threshold value in esorex is κ = 10, but to identify all induced hot pixels we set this κ = 6 to be sure that all cosmic/hot pixels are removed and no real data are clipped.

Wavelength calibration. UVES wavelength calibration errors are typically in the range of 0.1-0.5 km s -1 (Whitmore et al. 2010). To achieve an accurate wavelength calibration, the dispersion relation is obtained by extracting the thorium-argon arc lamp frames using the same weights as those used for the science objects. Therefore, first we reduced the science frames with an optimal extraction method to generate a pixel weight map. Then by applying this weight map to the wavelength calibration, an accurate wavelength calibration with a statistical error less than 5 × 10 -4 Å typically in all central wavelengths (for more details see Table 2) and a systematic uncertainty less than 1.7 × 10 -4 Å is achieved. To optimize the number of lines used in the wavelength calibration solution we tested a tolerance value ≃0.07 pixels to reject the line identification with wavelength residuals worse than the tolerance. For the final iterations of the fit of the wavelength calibration solutions we set the sigma-clipping to κ = 3. Further improvements to the wavelength calibration are being studied for the public release of the data. For example, there is a temperature and density dependent shift (which can be as much as 1 pixel and different for the different wavelength regions) in the position of the thorium-argon lines (UVES User Manual). This can be potentially corrected by taking into account the difference between the observation and calibration temperatures and pressures. Also, we foresee improvements to the final wavelength calibration in the 860 nm Red-U arm, where there are few thorium-argon lines, by cross-referencing with a model telluric transmission spectrum.

Blaze function and order merging. Previous analyses of data obtained with UVES demonstrate that the shape and position of the blaze function is the primary source of problems in the order overlap regions. The continuum changes vary smoothly over subsequent orders, which may be related to the fact that the blaze profiles produced by UVES are not the same as the theoretical predictions. Accordingly, the blaze function at the overlapping regions is not sufficiently well characterised, therefore by averaging the overlap regions some artificial features appear and cause the signal level to fall off at the end of the orders. Irregular variations in the continua at the edges of orders are likely due to a mismatch between the paths of the light from the star and the flat-field lamp [START_REF] Nissen | 2007 ESO Instrument Calibration Workshop[END_REF]) and result in discontinuities where orders have been merged.

For reduction "B", no blaze correction was performed. Optimal merging provided the best results in the overlapping regions for both reductions "A" and "B". Figure 6 shows a comparison of reductions "A" and "B" for HD 23180 in a spectral region with two overlapping orders. Reduction "B" reveals a mismatch of the order-overlapping regions, resulting in a jump in the spectra at 5855 Å not present in the reduction "A" spectrum.

Quality control. The S/N of the spectra as a function of wavelength was estimated by fitting a first order polynomial to regions of the spectra in bins of 1 Å and measuring the residual in each 0.02 Å wavelength bin. Table 3 lists the median S/N (per 2-pixel "resolution-element") for each setting, together with the respective continuum wavelength regions. In Fig. 7 we compare for HD 184915 the spectra obtained with the dedicated EDIBLES processing presented here and the default archive data products Notes. (a) Reduction "A". (b) Reduction "B".

(ADP) provided by ESO 2 . The increase in S/N (labelled in the figure) is almost a factor two for both the Red-L and Red-U spectra (cf. Table 1). For each target the different settings/arms generally have different S/N ratios. This is primarily due to (1) the choice of targets which are often brighter in V band compared to B band (i.e. reddening), and ( 2) the lower efficiency of UVES in the very blue and very red parts of the accessible wavelength range. In terms of S/N reduction "A" performs slightly better than reduction "B" for the 346-nm setting, both perform similar for the 437-nm and 860-nm settings, while reduction "B" performs better for the 564-nm setting. The main difference between the two reductions is in terms of the order-merging jumps.

In addition, there are small, though noticeable differences in the "noise" between both reductions. The reduction scheme "A" is adopted as the primary scheme for results shown in this work. Both reductions are therefore retained for reference and as a control for spurious features.

As an example, the final continuum normalised EDIBLES UVES spectrum of HD 170740 (from reduction "A") is shown in full in Fig. 8, where each panel corresponds to one of the instrument settings (Table 1). A closer view of this spectrum in shown in Appendix B. All processed EDIBLES spectra will be released as new "Phase 3" data to the ESO archive later in the project.

Telluric and stellar features

Earth transmission spectrum

A large number of weak and strong telluric oxygen and water absorption bands arise from molecules present in the Earth atmosphere covering nearly the full DIB range. In addition, for the near-UV spectral domain (300-350 nm) a correction for atmospheric ozone needs to be applied.

These telluric lines can be removed or modelled by using bright, early-type type star spectra recorded in the same conditions as the targets, or by using synthetic atmospheric transmittance spectra. The former method requires observing (unreddened) standard stars at roughly similar airmass, shortly before or after the primary science target observations. This procedure is not possible within the filler observation strategy, and as the EDIBLES targets are themselves bright this would double 2 These spectra are generated by ESO's Quality Control Group for all UVES point-source observations. The pipeline processing is done automatically through a dedicated workflow (with no fine-tuning of pipeline parameters specific to the needs of our programme). The 1d-extracted spectra are ingested into the ESO archive as so-called "Phase 3" data products. called rope-length minimization is used (Raimond et al. 2012).

Briefly, the algorithm searches for the minimal length of the spectrum that is obtained after division of the data by the transmittance model. To do so the column of the absorbing species, the Doppler shift and the width of the instrumental function by which the transmittance model is convolved are all tuned. The method uses the fact that when the telluric lines are not well reproduced, strong maxima and minima remain after the datamodel division and the spectrum length increases. On the contrary, if the modelled lines follow very well the observed ones, the corrected spectrum is smooth. An example of correction is shown in Fig. 9 for the 6284 Å DIB. Here the rope-length method has been first applied to the O 2 lines, then in a subsequent step the O 2 -corrected spectrum is corrected for the weak H 2 O lines that are also present in this spectral region.

For regions of stronger telluric lines, division by deep lines induces overshoot and the exact shape of the telluric profiles and of the instrumental function becomes crucial. The simple ropelength method alone is no longer appropriate. We have tested an iterative method that is a combination of the rope-length method and a classical fitting. In a first step we excluded all spectral intervals around the centres of the deep lines and performed a segmented rope-length optimization, i.e. the algorithm searches for the model parameters that minimize the sum of the lengths of the individual spectral segments. We then divided the data by the corresponding adjusted model and performed a running average of the divided spectrum to obtain an approximate stellar continuum. We then fitted the data to the convolved product of this continuum and a telluric transmittance. The instrumental function is now modelled as the sum of a Gaussian and a Lorentzian, which allows weak extended wings to be taken into account. The instrumental profile is the same for all lines within the corrected interval. The parameters defining these two components as well as the column of the absorbing species are free to vary during the adjustment. The data were then divided by this updated model. This process can be iterated and stopped when there is no longer any decrease of the "rope-length". An example of such a twostep correction is shown in Fig. 9 for the spectral region of the C + 60 9577 and 9632 Å DIBs. There are still some residuals, but these are limited mostly to the deepest, (partially) saturated telluric lines. This occurs particularly when these lines are slightly Doppler shifted or broadened due to atmospheric pressure in a way that is not fully predicted by the model. For a description of such effects see [START_REF] Bertaux | a webbased service of atmospheric transmission computation for astronomy[END_REF]. Nevertheless, the DIBs at 9577 and 9632 Å, assigned to be due to C + 60 as mentioned in the introduction, stand out clearly in the telluric corrected spectrum. The three weaker DIBs between 9350 and 9450 Å reported by Walker et al. (2015), though not yet confirmed independently (Galazutdinov et al. 2017;Cordiner et al. 2017) present significant challenges for detection due to the presence of strong, saturated telluric water lines in this spectral range. We intend to investigate the C + 60 bands in more detail later in the EDIBLES project, but this will depend upon the accuracy and success of the telluric line modelling for each line-of-sight (as there are numerous saturated atmospheric water absorption lines in this wavelength region) as well as the stellar atmosphere modelling required to account for, for example, contribution of Mg ii (as discussed in Galazutdinov et al. 2017).

Stellar spectra

In addition to the goals discussed above, the EDIBLES observations provide high-quality spectra of the target stars themselves. These span the full spectral range of early-type stars, from early O-type dwarfs through to late B-type supergiants (plus a couple of later/cooler stars). Published spectral classifications of the sample are summarised in Tables A. 1 and A.2. All of the O-type EDIBLES targets have been observed as part of the Galactic O-Star Spectroscopic Survey (GOSSS), a comprehensive survey of bright Galactic O stars at a resolving power of R ∼ 2500 (Sota et al. 2011(Sota et al. , 2014)). The detailed Ostar classifications quoted in Tables A.1 and A.2 are the GOSSS types -a thorough description of the classification criteria was given by Sota et al. (2011), including an overview of the various classification qualifiers used to convey additional information on the spectra (their Table 3).

In contrast, most of the B-type stars in the EDIBLES sample have not been subject to such morphological rigour with high-quality digital spectroscopy. Work is underway within the GOSSS to better define spectral standards and the classification framework for early B-type stars (Villaseñor et al., in prep.), with a few stars overlapping with the EDIBLES sample. Nonetheless, the high-quality, high-resolution spectra from EDIBLES will be useful to refine the classification framework for B-type stars, particularly compared to similar efforts in the Magellanic Clouds (e.g. Evans et al. 2015). Fig. 10. An illustration of the quality of the spectra for the 5780 and 5797 Å (left) and 6614 Å (right) DIBs. For each target all observed spectra were co-added in the heliocentric reference frame. The five targets shown have comparable E(B -V) values (Tables A.1 and A.2), and thus have similar 5797 Å DIB strengths. The well established, strongly variable 5780/5797 ratio can be seen in the spectra, with the intensity of 5780 absorption inversely related to the molecular gas fraction, f H 2 (Tables A. 1 and A.2). The five spectra are shown superimposed on each other at the top of the panels. Note that because of generally poor observing conditions there are numerous weak and narrow atmospheric water features present (particularly noticeable around 5788-5792 Å) that could influence the 5797 Å profile. In the future these features will be removed using the method described in Sect. 6.

In the short-term we will inspect the EDIBLES data in the context of classification, to update/refine spectral types as required -whether arising from the added information of the highresolution UVES data (cf. lower-resolution spectroscopy from the GOSSS, for example), or simply from intrinsic spectral variability, which is seen in many early-type stars. This will ensure the best parameters are adopted in estimating stellar colours, thence the line-of-sight extinctions. We will also look for evidence of spectroscopic binaries in targets with multiple observations (and/or relevant archival data, see, e.g., Sect. 7.4). Our longer-term objective is a quantitative analysis of the stellar spectra to determine their physical parameters (effective temperature, gravities, rotational velocities, mass-loss rates etc.), employing tools developed specifically for the analysis of earlytype spectra (e.g. Mokiem et al. 2005;Simón-Díaz et al. 2011). Ultimately this will help removal of stellar features from the EDIBLES spectra to aid analysis of the interstellar features.

Quality assessment: the interstellar spectrum

In this section we highlight the interstellar lines and bands observed for a few selected lines-of-sight.

EDIBLES

As noted above, the full spectrum of HD 170740 is shown as an example in Figs. 8 andB.1. For initial guidance in identifying the DIBs in this line-of-sight the average ISM DIB spectrum (Jenniskens & Désert 1994), scaled to E(B -V) = 0.5 mag, is shown in the latter figure. This reference spectrum includes broad DIBs not included in e.g. Hobbs et al. (2009) but which appear to be present in the observed spectrum.

In Fig. 10 we compare the three strong DIBs at 5780, 5797 Å (Heger 1922;Merrill & Wilson 1938) and 6614 Å for singlecloud lines-of-sight towards five EDIBLES targets, HD 149757, HD 184915, HD 144470, HD 145502, and HD 147165. The line of sight reddening, E(B -V), for these sightlines differs by less than 0.17 mag (cf. Table A .1). The spectra are averages of individual exposures co-added in the heliocentric rest frame and subsequently continuum normalised (but not otherwise scaled other than to offset them in the figure).

As expected, for sightlines with such small variations in reddening, the 5797 Å DIB profiles have similar central depths (insensitive to f H 2 ; [START_REF] Sonnentrucker | The diffuse interstellar bands at 5797, 6379 and 6613 Angstroms. Ionization properties of the carriers[END_REF]. The large variations in the strength of the 5780 Å DIB are thought to be related to variations in the local interstellar conditions. The sightline with the weakest 5780 Å DIB is more neutral as indicated by the large molecular fraction, whereas the sightline with the strongest 5780 Å DIB has the highest atomic column density. However, while the 6614 Å DIB of the former is also the weakest, the latter does not exhibit the strongest 6614 Å DIB, thus indicating that additional parameters must play a role in determining the DIB carrier column density.

EDIBLES versus CES

High-resolution (R = 220 000) spectra of the 6614 Å band have been recorded using the Coude Échelle Spectrograph (CES) fed by the fibre link with the Cassegrain focus of the 3.6 m telescope at La Silla Observatory (Galazutdinov et al. 2002). A signal-to-noise of ∼600-1000 was achieved. Comparison of the EDIBLES and CES data is shown in Fig. 11 for HD 184915, HD 144470 and HD 145502 and the main features are in good agreement. Of the three principal absorption components, the A76, page 11 of 29 Here the shape of the continuum and the number of clouds are determined in a new and totally automated way. To do so, the noise standard deviation is initially measured in regions adjacent to the sodium lines. Model adjustments are then made iteratively for an increasing number of clouds until the residuals in the sodium/calcium area become equivalent to the standard deviation. The prior radial velocity of each additional cloud is derived from the location of the minimum in the residuals from the previous step of adjustment. A representative example is show in Fig. 91. Note that in some cases such like the one from the Figure, the data may not be well fitted at the bottom of the lines, due to the existence of close blends, but even if the depth of the interstellar lines is not properly estimated, the velocity of each cloud component is accurately determined and the global shape of the modeled spectrum is most of the times adequate. This determination will be sufficient for DIB studies. However, sometimes the minimization procedure does not reach the absolute minimum values. This is the case of some multi-cloud sightlines with very saturated NaI-D 2 -D 1 lines. Failure to converge can be fixed by a manual/classical fit where we choose ourselves the minimas as illustrated in Fig. 92. This method has been applied to a dozen spectra that can be found in Figs. 18 and 19,appendix C. Table 91 lists the wavelengths of the interstellar lines and the number of successful automated fits. All spectra and fit results are presented in appendix C and were inspected visually to confirm the presence or absence of multiple velocity components. The flow chart in Fig. 93 outlines the types of decisions made after the visual inspection of all EDIBLES targets. 9. Towards a new DIB atlas

Searching for IS features

There are several methods to extract the interstellar information from stellar spectra. The historical one consists in the use of binary stars. Due to the stationnary nature of the interstellar lines, the comparison of spectroscopic binaries at different orbital phases allows to distinguish stellar absorptions from those of interstellar origin. Such a technique was used by Hobbs et al. (2008) to establish their DIB survey toward the double-lined spectroscopic binary HD 204827. The census of the DIBs by [START_REF] Herbig | The diffuse interstellar bands. IV -The region 4400-6850 A[END_REF], [START_REF] Jenniskens | A survey of diffuse interstellar bands (3800-8680 A)[END_REF], Tuairisg et al. (2000) and Hobbs et al. (2009) Bhatt, Cami 2015 were the first to perform a sensitive survey of interstellar lines in the near-UV by using a "superspectrum" in all kinds of reference frames (geocentric, telluric, stellar and interstellar). This useful tool was also presented in Chapter 6 for the near-infrared range. We make use of this method by looking at DIBs in a co-added spectrum of twenty single cloud targets. In this work in progress, an atlas based on a "superspectrum" (and also considering spectra of individual targets) will allow to confirm/reject some weak DIB candidates, to discover new ones, to give insight into line profiles and to update the previous surveys. A good example that highlights the feasibility of such technique in the framework of EDIBLES is the HD 170740 spectrum shown in Fig. 94 where DIB locations and widths from Hobbs et al. (2009) are overplotted. It is possible to see almost all of optical DIBs, allowing such features to be easily detected in a carefully constructed "superspectrum".

This technique was applied separately for the mono-cloud and multi-cloud samples. We will undoubtedly use the mono-cloud situation for the production of the atlas (in particular to characterize the profiles), but we deliberately kept the superspectrum of the multi-cloud targets. Since those are more reddened, they can confirm the existence of a potential interstellar feature that was too weak to be discerned in the mono-cloud "superspectrum" or in single observation. In the mono-cloud stacking case, the normalized spectra were shifted to their respective interstellar frame using the radial velocities already determined in Chapter 9.1. In the multi-clouds case it is difficult to assign to the sightline an interstellar radial velocity that would correspond to the dominant component, instead we performed a cross-correlation of each spectrum with the deepest line of the NaI doublet of a reference star (selected among the sample). Each star spectrum was then shifted by the amount deduced from the cross-correlation. These

Chapter 10

Characterization of DIB profiles

Many of the DIB carriers are thought to be large carbonaceous molecules in the gaseous phase, but except for the very likely identification of C + 60 (Campbell et al. 2015;Cordiner et al. 2017b;[START_REF] Spieler | {{{\rm{C}}} {60}}ˆ{+} and the Diffuse Interstellar Bands: An Independent Laboratory Check[END_REF][START_REF] Walker | The 9577 and 9632 Å Diffuse Interstellar Bands: {{\rm{C}}} {60}ˆ{+} as Carrier[END_REF]Lallement et al. 2018b), none of these bands has been definitely assigned to any given species.

Identifying the DIB carriers and measuring their response to physical conditions and chemical compositions of interstellar clouds is of high importance. This would add to our knowledge of the molecular inventory of these clouds in which molecules were detected with increasing numbers of atoms (up to 18) during the last decade, with a large gap however for the pure-carbon molecules between C 2 and C 3 (Schmidt et al. 2014) on one hand and the fullerenes C 60 and C 70 on the other hand, leaving considerable room for other species. It also would be key to understanding the life cycle of cosmic dust and carbonaceous compounds from dying star ejected envelopes to the birth places of new stars and planetary systems. Sub-structures in DIB profiles are indicative of the molecular structure of carriers and guide the data towards better comparisons with laboratory spectra. However, such measurements require high-performance spectrographs and to date their use has been restricted to relatively strong DIBs and a limited number of sightlines. In fact, disentangling the spectral components requires high spectral resolution, and, furthermore, is restricted to target stars for with only a small Doppler broadening along the line-of-sight -the so-called single-cloud situation, to avoid blends and sub-structures arising from nearly overlapping bands in different clouds. Moreover, the analysis of DIB profiles towards a carefully selected sample of spectra from different spectral types and corrected from telluric lines allows us to conclude with confidence that the observed substructures are neither telluric nor stellar in origin. The EDIBLES survey provides a sufficient number of such monoclouds spectra with high resolution and high S/N ratio and thus facilitates

Introduction

A sub-class of DIBs, the so-called C 2 -DIBs, is particularly interesting because they are detected in interstellar clouds that are characterized by high columns of the C 2 molecule (Thorburn et al. 2003;[START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF][START_REF] Kaźmierczak | C 2 and Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF], i.e., environments more closely chemically related to the cool, dense molecular clouds where stars form, as opposed to the more diffuse, warmer clouds that pervade the Galaxy. Profiles of several C 2 -DIBs and some other weak DIBs have been examined also by Galazutdinov et al. (2002) and [START_REF] Słyk | A search for fine structure inside high resolution profiles of weak diffuse interstellar bands[END_REF]. Asymmetries were found, but in the case of the weak C 2 -DIBs these were only seen for the 4963, 5418, 5512, 5541 and 5546 Å bands. Distinct sub-structures were found towards HD 147889 for the 4734, 5175, 5418, 5512, 5546 and 6729 Å DIBs by Galazutdinov et al. (2008).

In the case of the C 2 -DIBs, the study of their profiles is particularly difficult. The weakness of the absorption requires a very high signal-to-noise ratio (S/N), a careful correction of telluric lines and disentangling from weak stellar features. In addition, because they are narrow (∼ 0.5 Å), the single-cloud condition1 is even more stringent. Very high resolution spectra commonly reveal that an apparent single absorption can actually be decomposed into absorptions in several closely related clouds, reflecting the quasi-fractal nature of the dense interstellar matter [START_REF] Welty | A High-Resolution Survey of Interstellar K I Absorption[END_REF]). However, the clumps are generally fragments of the same complex, and often share a common environment and consequently possess very similar physical properties. Sightlines with high C 2 columns are crossing a cloud core with a density 100 cm -3 (higher than the typical diffuse interstellar medium (ISM)). It is a relatively rare situation, and therefore, the probability of crossing two independent cloud cores with identical radial velocities and with interstellar columns on the same order is low, i.e., C 2 sightlines with a single absorption offer a good probability of being dominated by a single structure. Detecting and studying the C 2 -DIBs is one of the goals of the ESO/VLT EDIBLES survey. EDIBLES is devoted to a deep analysis of the DIB properties and is particularly suited for the study of weak DIBs and their sub-structures, thanks to the high resolution of the UVES spectrograph, and, especially, thanks to the high S/N reaching 800 for a large fraction of the targets [START_REF] Cox | The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) . I. Project description[END_REF]). In addition, the large spectral coverage allows the simultaneous study of many gaseous absorption lines across the entire blue to the near-infrared range, like OH + that can be used as tracer for the cosmic ray ionization rate [START_REF] Bacalla | The EDIBLES survey IV. Cosmic ray ionization rates in diffuse clouds from near-ultraviolet observations of interstellar OH+[END_REF]).

Here we present an analysis of individual C 2 -DIBs in EDIBLES spectra 10.1. C 2 -DIBs profiles recorded along seven single-cloud and one multi-cloud lines-of-sight. We additionally show the results of a technique allowing to better reveal spectral sub-structures, namely the stacking of spectra of different target stars after they have been Doppler shifted to the cloud reference frame. Stacking spectra to enhance the S/N of band profiles has proved to be efficient to reveal DIBs (see, e.g. Lan et al. 2015), however, this is the first use of stacking in a search for profile sub-structures. In doing so, we have examined other absorption bands that may correspond to so far unreported C 2 -DIBs.

The observations, the telluric removal and the single-cloud selection have already been described in the previous chapters of this Part V. In Sect. 10.1.2 we describe the selection of the C 2 sightlines and in Sect. 10.1.3 the analysis of the C 2 lines. Sect. 10.1.4 presents the selected spectra in the intervening cloud frame, in the spectral regions of the C 2 -DIBs classified by Thorburn et al. (2003). We also show the spectral regions containing our potentially new C 2 -DIBs. In Sect. 10.1.5 we list the wavenumber intervals between the observed absorption sub-peaks, when measurable, and study their variability among the bands. In Sect. 10.1.6 we present our optimal profiles obtained by co-addition of the spectra of three targets that have relatively deep DIBs and very similar kinetic temperatures and show how these vary as a function of the kinetic temperature derived from the C 2 analysis. We compare the profiles of each C 2 -DIB towards several targets characterized by different C 2 excitation temperatures in Sect. 10.1.7. Finally, several correlational studies are presented in Sect. 10.1.8. They reveal a distinction between C 2 and non-C 2 DIBs. We discuss the various results in Sect. 10.1.9.

C 2 single-cloud sample selection

Highly reddened lines-of-sight typically intersect more than one interstellar cloud [START_REF] Welty | A High-Resolution Survey of Interstellar K I Absorption[END_REF]. Consequently, the resulting DIBs then represent some ill-defined average of the DIB properties along line-of-sight conditions, which complicates the interpretation. In the case of sub-structure studies, the existence of clouds at various radial velocities has the effect of smoothing and suppressing the structures. Therefore, we restrict our study of the Vos et al. 2011). e HD 169454 is not a single-cloud line-of-sight but it is used for comparison (c.f. Sect. 10.1.7).

HD 24398 and HD 149757 that show only one component at the same resolution. However, we keep HD 23180 in the analysis since the Doppler splitting is very low and we assume that the two velocity components belong to two clumps of the same cloud.

Table 101 lists their spectral type, radial velocity and rotation velocity as well as the line-of-sight reddening E(B-V), the total-to-selective extinction ratio R V , the molecular H 2 fraction f(H 2 ), and the heliocentric radial velocity of the dominant absorbing cloud. Note that we also added to Fig. 101, to Table 101 and to our study the target star HD 169454. As shown in Fig. 101, this line-of-sight does not represent a single-cloud situation, instead there are two main clouds with radial velocities differing by ≃5 km s -1 , and many more other smaller clouds. We have kept this target for comparison, and also because it is characterized by high reddening E(B-V), strong C 2 lines and deep diffuse bands.

Interstellar C 2 lines: detection and analysis

As stated above, the second requirement for our target sample is that they additionally possess detectable C 2 lines. All combined spectra have been visually inspected for the presence of the C 2 (1-0), (2-0) and (3-0) Phillips bands. This requirement greatly reduces the number of appropriate sightlines. Fig. 102 shows the (2-0) C 2 Phillips lines for the resulting sample. The (2-0) band has been chosen for further analysis due to its high oscillator strength, lack of contamination and apparent absence of CCD fringing residuals that adversely affect analysis of the (1-0) band.

Column densities for C 2 were derived using a custom fitting procedure written in Python. The Wavelength (Å) R( 10) R( 6) R( 4) R( 8) R( 2) R( 0) R( 12) Q( 2) Q( 4) P( 2) Q( 6) Q( 8) P( 4) Q( 10) P( 6) R( 14) R( 16

n log N(C 2 ) b v N(C 2 )/E(B-V) T 02 ref (K) (cm -3 ) (cm -2 ) (km s -1 ) (km s -1 ) (K)
HD 23180 44.7 +5.9 -3.6

346.5 Table 102, and the best-fitting spectral models are shown in Fig. 102. A complete description of all the EDIBLES C 2 spectra and their properties will be presented in a separate study (Cordiner et al., in prep.). Parameter uncertainties (±1σ) were generated using Monte Carlo noise resampling with 200 replications.

Extractions of the C 2 -DIBs

Extraction of individual C 2 -DIBs We have analyzed the spectral regions that correspond to the series of 18 DIBs classified as C 2 -DIBs by Thorburn et al. (2003), using the DIB wavelength centers listed in this work. For each sightline and each DIB, we have fitted a third order polynomial continuum around the DIB, normalized the spectrum based on this continuum, then determined the DIB equivalent width (EW) as the area between the spectrum and the normalized continuum. Figs. 103, 104, 105, 106, and 107 show the normalized spectra around each C 2 -DIB for the seven single-cloud targets and HD 169454. These figures also show our choice of the spectral areas on both sides of the band used for continuum-fitting as well as for the spectral interval used for the EW measurements. As stated in the introduction, it is essential here to identify weak and narrow overlapping stellar lines. In lieu of observing additional, lightly reddened stars of similar spectral types to be used as spectral standards, we used the eight targets together and the fact that stellar line widths must correlate with the rotations (at variance with DIBs). To do so we have used the full spectrum to estimate the rotational broadening for each target. We also used the rotation velocities vsin(i) listed in In the case of the 4727 Å DIB, it has a double structure suggestive of a blend with a stellar feature on its red side. However, the width of this structure appears to be independent of the stellar rotation. Rather, its shape is perfectly correlated with the main absorption. Therefore, we concluded that this absorption is part of the DIB or an additional, blended DIB, and we measured the total equivalent width of both structures, as shown in Fig. 103.

The situation for the 5003 Å DIB is quite different. There we find a blend with an additional absorption, however, this absorption is absent in three other sightlines. We conclude that this feature must be of stellar origin and we keep only the three clean bands.

We used the residuals of continuum fitting to estimate the standard deviation in each DIB spectral interval and for each target. This standard deviation was used in a Monte-Carlo noise resampling with 100 replications and we estimated in this way the uncertainty on each DIB EW. All EWs and their uncertainties are listed in Table 103. In exactly the same way, we measured the EWs and associated uncertainties for eight strong non-C 2 DIBs, and the results are also listed in Table 103. Their EWs are used in Sect. 10.1.8.

Inspection of Figs. 103-107 reveals clearly that sub-structures do exist for at least 14 DIBs. The shapes look very similar to those of the strong bands in previous work which have been studied in depth and found to be compatible with rotational contours of large molecules. There are strong differences from one band to another, with two or three individual branches, depending on the band, accompanied in some cases by a broad red wing. the profiles as molecular rotational contours, we assume that the upper state rotational constants are smaller than in the ground state. Selection rules can then result in different types of transitions resulting in either a two-peak profile similar to the 5797 or 6196 Å DIB profiles that then represent unresolved P and R branches or a three-peak profile that additionally displays a central Q branch. With these conventions, we have determined the peak positions of the sub-peaks (in wavenumber space; ν P , ν Q and ν R ) and we have extracted the wavenumber intervals (∆

ν PQ = ν Q -ν P , ∆ ν QR = ν R -ν Q and ∆ ν PR = ν R - ν P )
between them in all relevant cases. This was done based on visual selection of regions possessing a well-defined minimum or secondary minimum, and subsequent fitting of the bottom region of each sub-structure with a second order polynomial to extract the minimum location. The C 2 -DIB substructure peak separations ∆ ν PQ , ∆ ν QR and ∆ ν PR are listed in Table 104. These values are given in cm -1 , as this unit offer a wavelength independent value that allows a direct comparison between the different C 2 -DIBs. We did not attempt to fit the profiles with the product of individual components, as the choice of the number of components is quite subjective, especially in the case of the presence of red wings or for the weakest bands. According to the results of Table 104 and as can be seen for all DIBs in Fig. 108, the peak 10. Characterization of DIB profiles relative amplitudes and peak sub-structure separations are quite different among the DIBs. The separation between the P and R branch scales roughly with B × T, where B is the rotational constant and T the carrier's rotational temperature (but see [START_REF] Huang | Constraining the size of the carrier of the λ5797.1 diffuse interstellar band[END_REF]. Therefore for the same cloud and within the assumption of the same physical conditions, this observed variability implies that the sizes of the carriers for the different C 2 -DIBs are most likely quite different.

Prominent sub-structures and average spectral profiles

The generally low signal strengths do not allow clear identifications of substructures for all combinations of DIBs and targets. A classical method used to increase the S/N and enhance weak features is the co-addition of profiles. Some caution is needed here since different sightlines may correspond to different shapes of the same DIB, in this case co-adding sub-structures is not physical. This could be the case if the carrier internal population distributions were markedly different from one sightline to another in response to different excitations. Indeed, such peak-to-peak variations have been observed and used to place constraints on the rotational excitation temperatures of the 6614 and 6196 Å DIB carriers (Cami et al. 2004;[START_REF] Kaźmierczak | Excitation temperature of C 2 and broadening of the 6196 Å diffuse interstellar band[END_REF]. In order to test the co-addition feasibility, we have used the sub-peak intervals of Table 104 to estimate upper limits on the profile variability. We discarded all cases for which peak determinations were too uncertain, which resulted in the selection of only five bands. For each type of interval, we normalized it by simply dividing by the mean value of the sub-peak intervals over all eight targets. Fig. 1010 displays the resulting normalized intervals between the sub-peaks, here as a function of the kinetic temperature T kin for the corresponding target, derived from the C 2 analysis. Fig. 1010 shows that the variations from one sightline to the other for the same band are significantly smaller than the dispersion among the normalized intervals of the various DIBs for the same target, although a weak increase with temperature is discernible. It is difficult to draw firm conclusions from this, given the uncertainties in our sub-peak wavelengths. Three stars, namely HD 23180, HD 203532 and HD 147889 have the deepest absorption bands and are characterized by a good S/N, thus have relatively clear sub-structures. Fig. 1010 confirms that for these three targets (marked by arrows) the sub-peak interval variability is the smallest. For this reason, we performed a co-addition of the profiles over these three targets only. Before the co-addition, the normalized DIB profiles were shifted to a common interstellar rest frame and elevated at a power in such a way that their depths are the same for the three targets. The average profiles are displayed in Fig. 108. Although they can not be considered as pure intrinsic profiles (as stated above, rotational contours may change from one target to the other), these average profiles are free from telluric or stellar contamination and we believe they are our most accurate representation of each DIB structure. Such information complements well that contained in the series of individual measurements. On the other hand, it is often difficult to preclude profile variation and for this reason we discuss the DIBs towards each of the three selected stars separately in the next section.

Search for profile variability among the sightlines

With the goal of going one step further in the validation of the average profiles, we investigated the potential variations of DIB profiles for the three selected single-cloud targets. In particular, measurements of the C 2 molecule rotational temperatures T 02 defined as the average excitation temperature of the two (resp. three and four) lowest rotational levels of the ground electronic state have been performed by [START_REF] Kaźmierczak | Excitation temperature of C 2 and broadening of the 6196 Å diffuse interstellar band[END_REF] and Kaźmierczak et al. (2010a) for two of these single-cloud sightlines. The temperatures are different: T 02 = 20 K for HD 23180 and 49 K for HD 147889.

Figs. 1011, 1012 and 1013 show the 18 C 2 -DIB profiles for the three EDI-BLES targets after normalization to reach the same maximum depth. We also show the normalized profiles for the multi-cloud target HD 169454. It is interesting to disentangle the effects of Doppler broadening due to cloud multiplicity and broadening due to increase of the excitation temperature. Coincidentally, T 02 = 23 K for HD 169454, is very similar to the value measured for HD 23180. Moreover, profiles of the 6196 Å DIB have been investigated for this star as well as for HD 147889 and HD 23180 by [START_REF] Kaźmierczak | Excitation temperature of C 2 and broadening of the 6196 Å diffuse interstellar band[END_REF]. In order to better visualize differences, we show the profiles that correspond to HD 147889 superimposed on the three others because for this sightline the sub-structures are the most prominent. After careful inspection of all comparison figures, we identified three categories of DIBs.

The first category corresponds to the existence of sub-structures for the four targets, and no detected variation in the total width of the band nor in the locations of the sub-peaks. Falling in this category are DIBs 4727, 4734, 4963, 4984, 5418, 5512 and 5541 Å (Fig. 1011) confirming an earlier result by Galazutdinov et al. (2008) for the 4963, 5418 and 5512 Å DIBs. Therefore, for those bands the stacking is justified as a means to enhance the profile structures. It is worth noting that for 4984, 5512 and 5541 Å DIBs the substructures are smoother in the case of lower temperatures. The second category corresponds to the weakest bands for which individual profiles do not clearly reveal sub-structures well above the noise and no apparent variations of the total width. In those cases, the co-addition is justified since it simply improves the S/N. This is the case for the 4969, 4979, 5003, 5170 DIBs (Fig. 1012). In the case of the 5762 and 5793 Å DIBs the co-addition does not bring any improvement. We believe this may be due to the effect of telluric residuals (see the remark on the HITRAN database in Sect. 8.1 of Chapter 8) that become very important for targets with the weakest absorbing column. Note that these are the two weakest of all C 2bands. Further measurements and improved telluric corrections are needed to confirm their sub-peaks.

The third DIB category corresponds to the detection of profile variations among the four targets, and this may potentially reveal a response to the excitation temperature. Indeed, the full DIB width appears smaller for HD 23180 (T 02 = 20 K) and, to a lesser extent, for HD 203532 (unmeasured T 02 ) compared to HD 147889 (T 02 = 49 K). Interestingly, these DIBs are also narrower for the multi-cloud star HD 169454 (T 02 = 23 K) than for HD 147889, an effect opposite to the expected broadening due to cloud multiplicity. Moreover, the amplitudes of the sub-peaks appear smaller for HD 23180, HD 203532 and HD 169454 compared to HD 147889. Falling in this category are the 4363, 5541, and 5769 Å DIBs. Identical, but less clearly measured effects can also be seen for DIBs 5175, 5546, and 6729 Å. If these simultaneous variations in width and peak amplitudes are actually linked to the variation of the rotational excitation, then for these DIBs the average profile does not correspond to any intrinsic profile. However, we have kept these average profiles in Fig. 108. Interestingly, the DIBs from this third category are all very tightly correlated with the C 2 column, as discussed in the next section.

Correlational studies

Correlations with the C 2 column Our combined measurements of C 2 columns and weak DIB equivalent widths towards single-cloud targets constitute an ideal dataset for the study of any link between the C 2 molecule and each DIB carrier. Fig. 1014 displays the equivalent widths of the recorded C 2 -DIBs as a function of the C 2 column density N(C 2 ). We have superimposed on each figure the corresponding previous measurements from [START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF]. We did not include the 5003 Å band which was not studied by [START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF] and for which we have only three valid data 10. Characterization of DIB profiles points due to heavy stellar contamination. It can be seen that all our data points fall within the set of points from these authors, revealing good agreement with all these measurements. On the other hand, it can be seen that the correlation with N(C 2 ) is significantly tighter in the case of our targets. We interpret this as a consequence of our accurate determination of N(C 2 ), the excellent signal-to-noise of EDIBLES spectra, our careful selection of monocloud sightlines and correction for telluric and stellar features. Note that we have also included the multi-cloud sightline HD 169454 in these figures. The corresponding data point always falls within the [START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF] set of points. However, for many DIBs it is the most discrepant point if one considers our results only. This is a strong indication that the C 2 -DIBs are very well correlated with the C 2 molecule, and that a large fraction of the dispersion around the linear relationship is due to cloud multiplicity.

Respective influence of C 2 column and reddening The existence of a good correlation with N(C 2 ) is not a sufficient condition for qualification as C 2 -DIB and this has been discussed in detail by [START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF] and [START_REF] Kaźmierczak | C 2 and Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF]. One way to establish this qualification is to compare the dependencies of the measured EWs on both the C 2 column and another parameter representing the quantity of interstellar matter, e.g. the reddening or the gas column. A C 2 -DIB is one that is more tightly correlated with N(C 2 ) than with these other parameters. Inspired by the study of En- sor et al. (2017a), we have used our new EDIBLES dataset of C 2 and non-C 2 DIBs to perform a simplified parametric analysis restricted to the reddening and N(C 2 ).

For each DIB we performed a linear regression between the DIB EWs and both E(B-V) and the column N(C 2 ):

EW = a × N(C 2 ) + b × E(B -V), ( 10.1)
We took into account uncertainties on EWs but assumed E(B-V) and N(C 2 ) are known with accuracy. We then computed A = a/σ(a) and B = b/σ(b), σ(a) and σ(b) being the standard deviations found from the adjustment for the partial derivatives a and b. Since E(B-V) and N(C 2 ) are assumed to be accurately defined, the dimensionless quantities A and B represent the tightness of the links between the DIB and each of the two variables E(B-V) and N(C 2 ). We then normalized the vector A,B in the system of reference N(C 2 ) and E(B-V) by dividing A and B by (A 2 + B 2 ). Fig. 15 is a representation of the corresponding "vectors". The orientation of each vector points to the parameter that governs the DIB strength (for vectors oriented vertically or horizontally) or, in intermediate cases, represents their respective influence. Note that we performed the regression with and without inclusion of HD 169454 (the multi-cloud target) and of the highly reddened HD 147889 to test their influence. We find that the regression results do not change significantly except for the weak 6729 Å band that has clearly the most contaminated spectrum (see Fig. 107). We removed this DIB from the regression and show the results that were obtained using all targets. Fig. 15 reveals a gap between two series of DIBs. The first series corresponds to strong positive correlation with N(C 2 ) and weak or even negative correlation with E(B-V). The list of bands from this series is in total agreement with the 17 C 2 -DIBs reported by Thorburn et al. (2003) in their Table 4.a as their main C 2 -DIBs. In terms of relative correlation with C 2 , the rankings of all six C 2 -DIBs which are common to the two studies indeed agree to within the stated uncertainties. Moreover, our most tightly correlated 4734 and 5769 Å bands are also those noticed by these authors as their main C 2 -DIBs. Remarkably, the E(B-V) correlation coefficient for those two bands is found to be negative. The second series corresponds to the strong DIBs that follow the reddening E(B-V) rather than C 2 . At the margin of the C 2 -DIBs group is the particular 5850 ÅDIB. Interestingly, it has been classified as non-C 2 DIB (Kaźmierczak et al. 2010b) and as C 2 -DIB [START_REF] Kaźmierczak | C 2 and Diffuse Interstellar Bands // The Diffuse Interstellar Bands[END_REF]) in subsequent analyses. This band is definitely peculiar and deserves further study. Finally, we note that 4727 and 5762 ÅDIBs seem to be similarly correlated to E(B-V) and N(C 2 ). 10.2. Profiles of strong DIBs three sub-peaks in at least 14 DIBs, the remaining two being too weak to draw a definite conclusion. The two and three sub-peak profile structures are consistent with unresolved rotational branch structures of a molecular carrier. The two sub-peak profiles are very similar to those of the strong 6196 or 5797 Å DIBs, while the three sub-peak profiles are very similar to the one of the strong 6614 Å DIB. The profiles of the 18 C 2 DIBs were studied as a function of the transition and as a function of the sightline:

• Profile variability among the C 2 -DIBs for the same sightline:

The differences in peak relative amplitudes and peak sub-structure separations found among the 18 C 2 bands suggest that they correspond to different molecular carriers or electronic transition symmetries, and factors governing the level populations.

• Profile variability among the targets for the same C 2 -DIB: Three C 2 -DIB categories were derived. Several C 2 -DIBs appear to keep identical profiles among targets characterized by different C 2 rotational temperatures, however, for others, there is a potential detection of a response to the temperature, namely a global broadening of the absorption band and slightly larger separations between the internal sub-structures at higher temperature. Such effects are similar to those detected for the 6614 Å band by Cami et al. (2004) and for the 6196 Å band by [START_REF] Kaźmierczak | Excitation temperature of C 2 and broadening of the 6196 Å diffuse interstellar band[END_REF], and are likely associated with shifting Boltzmann maxima of rotational branches of the molecular carrier. Finally, for a fraction of the DIBs, the variability (or absence of variability) could not be assessed due to their weakness.

Profiles of strong DIBs

Most of the strong and narrow optical DIBs have been found to have asymmetric profiles, and in the case of the strongest ones such as DIBs 5797 and 6614 Å (Sarre et al. 1995;Ehrenfreund, Foing 1996;Galazutdinov et al. 2002), 5850, 6234 and6270 Å (e.g. Krełowski, Schmidt 1997;Galazutdinov et al. 2002), 6376, 6379 and6196 Å (Walker et al. 2001), sub-structures have been clearly identified which may be consistent with unresolved rotational branch structures associated with electronic transitions of gas-phase molecules. Figures 16,17,18,[START_REF] Ensor | A Principal Component Analysis of the Diffuse Interstellar Bands[END_REF]20,21,[START_REF] Bok | Dimensions and Masses of Dark Nebulae // Harvard Observatory Monographs[END_REF] and 23 represent our first attempt to find the best observed profiles of the 5780, 5797, 5850 and 6196, 6234, 6376, 6379 and 6614 DIBs. The method for the extraction of these strong DIBs has already been described in the previous section in exactly the same way we used for the intriguing subset of weaker DIBs, the so-called C2-DIBs. Chapter 11

Conclusion and perspectives 11.1 General conclusions

Four years ago, N. L. J. Cox, J. Cami (2014) published a road map following the excellent contributions of the International Astronomical Union symposium (IAU 297) devoted to DIBs. They pointed out the importance of spectral and sightline surveys by introducing for the first time the two concepts of "all DIBs, few targets" and "few DIBs, many targets". This thesis is along these lines insofar as it takes advantage of both approaches (and beyond). Each kind brings a new statistical or tomographic spectroscopic view at three very different scales in program size, namely (i) the APOGEE massive survey by using a single NIR DIB and then few additional weak NIR DIBs, (ii) the High S/N and large spectral coverage EDIBLES survey, (iii) and a program of a multiplex instrument (KMOS) in a restricted sky region.

In this thesis work I improved or developed several techniques adapted to observational spectroscopy from the near UV to the infrared, in particular by means of massive surveys, with the goal of extracting as most as possible and as reliable as possible measurements of DIBs. The developments helped to surmount the following difficulties:

• the identification and separation of stellar, interstellar and telluric signatures,

• the handling of massive datasets, and the need to use automated analyses.

In practice, I made use of the following recent advances:

• Considerable improvement in stellar models allowing the use of cold stars.

• Considerable improvement in telluric models allowing the detection (or rejection) of new features.

• New multi wavelength tools for target selections.

The combination of the new techniques, tools and models has made possible to take advantage of the large amount of available data from massive stellar spectroscopic surveys (APOGEE), from the excellent, high S/N ratio data from dedicated DIB surveys (EDIBLES) or from specific exploratory programs (KMOS/B68).

Novelties

1. Thanks to sightlines surveys, massive data were exploited. Subsequently, catalogs were produced and numerous statistical studies were carried out in order to compare the DIBs strengths with the environmental conditions and the properties of their hosting clouds (APOGEE catalogs and the skin effect illustrated by Planck).

2. The second novel aspect is the tomographic one. The Barnard 68 study is the first tomography of a DIB carrier in a single cloud. On the large scale, the construction of 3D ISM maps based on composite data, namely reddening and DIBs (Capitanio et al. 2017) and the 3D tomography based on APOGEE DIBs are the first of this type. Such tomographic studies constitute prototypes of future three-dimensional studies of DIB carriers with JWST's NIRspec or with future ground surveys or dedicated programs (SPIROU, WEAVE, 4MOST, MOONS...).

3. Prior to EDIBLES measurements, only a few DIBs had been observed with high enough signal and resolution to disentangle their spectral substructures. In the case of EDIBLES, not only the signal S/N is unprecedentedly high, but a careful selection of appropriate targets has also been done to extract reliable profiles, the single-cloud selection, i.e. sightlines characterized by a small Doppler broadening and where only one (or one dominating) interstellar cloud determines the DIB spectrum. Combined with the new APOGEE profiles and also the new DIB candidates from both surveys, this represents a new source of potential identifications.

4. New potential identifications require confirmation and additional inspection of data in a search for weaker or simply overlooked features.

The second novel aspect of this thesis resides in the combined stellar, interstellar and telluric technique improvements and the use of a superspectrum technique, i.e. the comparison of selected spectra co-added in the stellar and interstellar frames. This is mandatory to place constraints on the additional features and the confirmation (or refutation) of observed features. The main goal is the building of the most comprehensive and reliable atlas of DIBs.

11.3. Perspectives

Perspectives

Progress in DIB-focused research will undoubtedly come from an active synergy between theoretical work, astronomical observations and laboratory measurements from the UV to the IR spectral domains. In the laboratory, the interstellar conditions reigning in the diffuse ISM can realistically be simulated and DIBs carrier analogues can be generated from the vast collection of possible candidates1 .

The laboratory match with astronomical observations can be optimized if the sheer number of possible species to be studied in the laboratory can be constrained under relevant and realistic space conditions. The preselection can be done through in-depth studies of the physical and chemical properties of the DIB carriers based on a robust data analysis of high spectral resolution observations.

In this respect, the advent of massive surveys opens a new era and new possibilities for future detections and/or exclusions of certain DIB carriers and confirmation/rejection of PAHs/Fullerenes hypothesis.

In the future, the main goal will be to provide quantitative information and data analysis for interpreting observations from ground-(SPIROU/CFHT, EDIBLES/ESO-VLT, NASA IRTF and NOAO Kitt Peak Telescopes), airborne-(SOFIA), and space-based observatories -Hubble Space Telescope (HST) Imaging Spectrograph (STIS) or James Webb Space Telescope (JWST) Near Infrared Spectrometer (NIRSpec) -in order to derive robust constraints and to reduce the list of proposed molecules studied in the lab. On the other hand, the near-UV part of the EDIBLES data is the object of ongoing studies that should bring new constraints on the existence of DIBs in this domain, and subsequent constraints on the carriers. The NIR window with HST will also bring some constraints of the IR C 60 bands. Note that the resolution of STIS at 0.9 -1.0 µm is ∼ 10 000 and a S/N ∼ 700 can be achieved with special techniques.

These studies would be possible to extend to the mid-infrared (MIR)2 thanks to the advent of SOFIA Third Generation instrument, the High-Resolution Mid-Infrared Spectrometer (HIRMES) that will cover the [25 -122] µm spectral range with resolving powers ranging from 600 to 100 000 and the Echelon-Cross-Echelle Spectrograph (EXES) that operates in the 4.5 -28.3

Conclusion and perspectives

µm wavelength region, at high (R ∼ 50 000 -100 000), medium (R ∼ 5000 -20 000) and low (R ∼ 1000 -3000) spectral resolution.

Indeed, the advent of multi-object spectrographs such as JWST's NIRSpec and SPIROU (R=70 000) will provide massive amounts of data and new detections that will serve the basis for a wide range of analysis about NIR DIBs. The multi-object mode allows for simultaneously observing more than 100 sources over a field of view larger than 3 arcmin square, and NIRSpec will be the first spectrograph in space that has this capability. In addition, through the DIBs there is a direct connection between the DIBs and prominent features in the C 60 bands that will be studied by JWST. In this context, more correlations with IR bands are expected with the new laboratory investigation of the NIR and MIR spectral signatures of PAHs and derivatives.

DIB-based maps and their comparison with radiation and 3D dust and gas distributions open new windows to better understand the conditions of formation and disappearance of DIB carriers. Future surveys (from ground and space), resulting databases, and environmental and tomographic studies will bring more and more stringent constraints on the DIB formation and destruction/ionization/accretion sites. Particularly promising is the GAIA mission that considerably helps the tomography by providing distances with unprecedented accuracy, a huge database of extinctions and a huge number of 8620 Å DIBs. Refining our understanding of DIB carriers is also crucial to understand the interstellar lifecycle and at what stage they are involved in the chain and process of star formation. An approach to tackle these open questions, consists of studying the tomography of dense clouds with JWST's NIRSpec as already done with the dense cloud Barnard 68. All these investigations will open the possibility to shed light of whether, how, when, and where the DIB carriers are formed in interstellar clouds. The ultimate goal of the future research is to compare new high-quality observational data obtained through innovative signal analysis approaches with the unique set of laboratory data available to provide solid constraints on the nature of the DIB carriers and, possibly, the identification of DIBs with specific molecular carriers. A result that will have profound implications for our understanding of the Molecular Universe.

In light of the many results that were obtained as part of the thesis, there are many implications that this work might have in shaping a vision for future laboratory astrophysics investigations. The results obtained as part of the thesis have many potential implications for future laboratory investigations or observational programs.

• Spectroscopy:

The most important input is accurate data on wavelengths and line profiles (bandwidths) -this is what we can compare the laboratory data with. With 400 DIBs in a domain of ∼ 600 nm, chances of a coincidence 11.3. Perspectives are rather high, but a coincidence does not need to be a match. Here, extra-observational data can be really helpful in identifying bands unambiguously. See e.g. figs 3-9 in Schmidt et al. (2014).

In the case that the data allow to resolve rotational profiles (separate P/R branch or wings that give information on the Delta B's involved), astronomical observations could give a clue for the size of the molecule.

See for e.g. the paper by [START_REF] Huang | Constraining the size of the carrier of the λ5797.1 diffuse interstellar band[END_REF] where the authors assigned a double peak DIB structure to a 2P-X2P transition assuming a chain length of 6-8 atoms. The C2-DIBs study is another example, where the identification of sub-structures may add more info on the structure of the underlying carrier. This then can be tested in the laboratory, by selecting precursors that may result in species absorbing in the observed wavelength domain with almost the right spectroscopic properties.

• Environmental conditions:

By exploring environmental conditions by means of observations of other (non-DIB) species in parallel to the DIBs themselves, one may determine those parameters that favor DIB formation or disappearance: radiation fields (ions vs non-ions), existence of shockwaves, presence of molecules that may hint for a chemical network. Not only optical but also IR, sub-mm and radio astronomical data can be helpful to further paint the picture. Ideally, one may obtain correlations between such intensities of a specific DIB and quantities of a particular molecule (similarly to the still unexplained C2-DIBs case).

• Alternative clues?

Band-band correlations and relative intensity levels among bands suspected to be due to the same carrier: Refined observations with surveys could better assess correlations or absence of correlations between bands and their relative levels. These are crucial inputs for potential identifications of absorbing species (any absence of a band predicted with high intensity contradicts an identification based on a band with smaller intensity).

Band-dust grain relationships: Improved correlative studies between DIBs and grain properties (based on reddening curves or polarization) could give clues for DIB carrier accretion onto grains and coagulation. van Loon J. T., Bailey M., Tatton B. L., Maíz Apellániz J., Crowther P. A., de Koter A., Evans C. J., Hénault-Brunet V., Howarth I. D., Richter P., Sana H., Simón-Díaz S., Taylor W., Walborn N

2 1. 2 .

 22 Figure 12 Hubble's view of the Eagle Nebula. Credit: NASA, ESA, and The Hubble Heritage Team (STScI/AURA).

  DIB becomes stronger towards σ-type clouds. (e.g. Tielens 2014).

  Figure 111 Example of PAH infrared features (labeled in red) in the 2-20 µm emission spectrum of IRAS21282+5050 (a young planetary nebula). The observed data (shown in light dotted curve) have been fitted by a number of discrete features (black) and plateau features (green) above a continuum (blue). From Kwok, Zhang (2013).

  new adjustment methods for stellar spectroscopic surveys that have been developed and fully automated to extract as much information as possible from stellar spectra. I show their application to the near infrared (NIR) spectra of the SDSS/APOGEE H-band survey and the extraction of the 15 273 Å DIB. I started with the earlytype stars that were used for the elimination of telluric lines in Data Release 12 (DR12) without being part of the scientific targets, then I continued with the full dataset of science and calibration targets of DR12 belonging to the TYCHO catalog and, finally, with all the science targets consisting mainly of red giants of DR14.

Figure 22

 22 Figure 22 Example illustrating our fitting method. Data as provided by the APOGEE survey are shown in black while the final stellar+DIB modeled spectrum appears in magenta. The initial stellar model provided by APOGEE and the model obtained after application of the scaling factor α are shown in solid blue and dash-dot green lines respectively. The DIB absorption alone is represented with a dashed orange line. A grey band in the red part of the spectrum shows an example of masked region.

Figure 25

 25 Figure 25 Histogram of the Doppler velocities for all fitted lines (red) and for selected DIBs only (blue). The limiting values used in the first selection step are shown as black vertical lines. Note the exclusion of all unrealistic Doppler velocities falling in the first and last bins and the disappearance of the secondary maximum at ≃ 130 km s -1 that corresponds to false detections produced by a strong stellar line falling at this wavelength.

Figure 27

 27 Figure 27 Example illustrating our construction of a virtual DIB for a typical LOS with 6 HI clouds. Vertical lines in different colors mark the strength and velocity of the 6 individual HI 21cm components. The corresponding individual Gaussians for the synthetic DIBs are shown using the same colors.The function representing the total virtual DIB through the HI disk is plotted in black. Note that for a better visualization, the scales for the total DIB and individual components are different.

Figure 210 Figure 211

 210211 Figure 210 Same as Fig 25 for the DIB width σ. The limiting value 0.7 Å used in the selection step is shown as a black vertical line.

2 .Figure 212

 2212 Figure 212 DIB equivalent width as a function of the virtual DIB equivalent width that is proportional to the total HI column. The dotted red line is the one-to-one relation (see text). The color code is the same as in Fig 24 with 2 additional colors indicating the special cases of Be stars (orange) and stars from field 4529 (camel) from subsection 2.4.2. A clear detection limit is found at around 9m Å, indicated by a pink dashed line.

  final global fit, the DIB extraction code performs preliminary series of automated tests that allow to measure the residuals between the data and the adjusted stellar model in three spectral regions: the local region around the DIB A = [λ c -10-λ c + 10] Å, a region free of DIBs and telluric residuals B = [15 911-15 966] Å and a narrower region free of DIBs and telluric residuals and closer to the DIB C = [15 200-15 239] Å. The associated standard deviations R A , R B and R C are used for selection criteria and estimates of the DIB uncertainties.

Figure 41

 41 Figure 41 DIB equivalent width as a function of the color excess for the subset of 221 targets described in sect. 2.5. The linear relationship (red curve) is the one fitted to the whole set of APOGEE late-type stars by Zasowski et al. (2015).

Figure 43

 43 Figure 43 Example of future 3D mapping based on DIBs: DIB strengths and target Hipparcos distances for the two regions A and B from Figure 42. Blue (resp. red) markers correspond to a DIB equivalent width lower (resp. higher) than a threshold of 50 m Å. The closest (resp. most distant) targets show consistently negligible (resp. strong) absorption. Accurate and more numerous parallaxes should allow to bracket the distance to the clouds.

Fig. 1 .

 1 Fig. 1. Location of the targets from the LVV catalog compilation (green crosses) and of the APOGEE target stars retained for the DIB extraction and the inversion (violet signs). Coordinates are Galactic. Also shown are the directions of the molecular clouds of the Schlafly et al. (2014) catalog mentioned in Sect. 5.2 (red signs).

•Fig. 2 .

 2 Fig. 2. Illustration of the various categories of DIB extraction: a) detected; b) recovered; c) narrow; d) recovered and narrow; e) upper limit. APOGEE spectra are shown with solid black curves. The initial stellar model provided by APOGEE and the model obtained after application of the scaling factor are shown in solid blue and green respectively (note that in many cases they are very similar). The DIB absorption alone is represented in orange. The solid magenta curves represent the final stellar+DIB modeled spectrum.

2 ×

 2 RA -1.5× max(RA, RB, RC)] Narrow recovered DIBs

  Notes. RA, RB, RC are the standard deviation in three spectral ranges A = [λ c -10 Å; λ c + 10 Å], B = [15 890-15 960] Å and C = [15 200-15 250] Å, respectively.

Fig. 3 .

 3 Fig. 3. Galactic plane cut in the very low-resolution 3D opacity distribution to be used as a prior in the inversion. It is based on Pan-STARRS reddening measurements of Green et al. (2015). The Sun is at (0, 0) and the Galactic center to the right. Longitudes increase counter-clockwise. Units are mag pc -1 .

Fig. 4 .

 4 Fig. 4. Galactic plane cut in the 3D opacity distribution resulting from the four successive inversions: panel a: A distribution: color excess data alone and Hipparcos or photometric distances. Panel b: B distribution: same as A except for the replacement of 80% of the initial distances with Gaia-DR1 (TGAS) values. For the 20% of stars without Gaia distance we kept the previous value. Panel c: C distribution: same as B except for the addition of DIB-based color excess estimates for stars with a Gaia-DR1 parallax. Panel d: D distribution: same as C except for the use of a Pan-STARRS based prior distribution instead of a homogeneous distribution. Color scale, direction of Galactic center, and Sun position are the same as in Fig. 3.

Fig. 5 .

 5 Fig. 5. Planar cut along the Galactic plane in the inverted 3D distribution D shown in Fig. 4, panel d. The color scale is same as in Fig. 3. Top: the entire 4000 pc × 4000 pc computed area. Bottom: zoom in a 1000 pc × 1000 pc area around the Sun.

Fig. 6 .

 6 Fig.6. Local cavity boundaries: the distance at which a color excess of 0.015 mag is reached is plotted as a function of rectangular Galactic coordinates. In very weakly reddened directions for which 0.015 mag is not reached within our computation domain, the distance to the domain boundary is plotted after conversion to negative values. This allows us to distinguish between the two cases well. The figure illustrates well the local warp of gas associated with the Gould belt, i.e., more matter at positive latitudes in the Galactic center hemisphere and at negative latitudes in the anticenter hemisphere. It also reveals very clearly the local bubble openings to the halo at about 210-240 • longitude.

Fig. 7 .Fig. 8 .

 78 Fig. 7. Full-sky 2D map of opacity distribution integrated from the Sun to the boundaries of our computational domain (abs(Z) = 300 pc, abs(X) = 2 kpc, abs(Y) = 2 kpc). Top: LVV results are shown (distribution A). Bottom: the final inversion with APOGEE data and the Pan-STARRS based prior distribution are shown (distribution D). Isocontours from Schlegel et al. (1998) for E(B -V) = 0.025 and E(B -V) = 0.32 are superimposed as dotted and continuous black lines. The map is in Aitoff Galactic coordinates. Longitudes decrease from +180 • to -180 • from left to right. The color scale refers to E(B -V), linear, in units of mag.Table 2. Evolution of the prior and final χ 2 per line of sight in the four inversions. Distribution Data Initial χ 2 Final χ 2 A LVV catalog, Hipparcos+phot 5.361 1.100 B LVV catalog, Gaia-DR1+phot 5.810 1.085 C LVV catalog + APOGEE, Gaia-DR1+phot 5.080 1.023 D LVV catalog + APOGEE, Gaia-DR1+phot, Pan-STARRS prior 3.521 1.006 A65, page 9 of 17

  Fig. 9. Comparison between the three B-C-D distributions (see Sect. 4 and Fig. 4) for a vertical plane along l = 212 • . The X-axis is within the Galactic plane, the Y-axis is toward the North Galactic pole direction. The color scale is the same as in Fig. 3. Iso-contours are superimposed for ρ 0 == 0.0002, 0.00035, 0.00076, 0.001, and 0.002 mag pc -1 . The green crosses in top panel are the positions of the targets from the LVV catalog in a slice of 10 degrees around the considered vertical plane, violet crosses in middle panel are the targets from the APOGEE catalog, and the black circles in bottom panel are the molecular clouds positions and associated uncertainties based on the catalog of Schlafly et al. (2014).

  Fig. 10. Same as Fig. 9 for the vertical plane along l = 180 • (anticenter region).

  Fig. 11. Same as Fig. 9 for the vertical plane along l = 110 • (Cepheus region).

Figure 7

 7 shows 2D color excesses from inversions A (top map) and D (bottom map), which are computed by integrating local opacities from the Sun to the boundaries of our computational domain; the distance to the Galactic plane is smaller than 300 pc and distances along the X and Y axes are smaller than 2000 pc. Iso-contours from Schlegel et al. (1998) for E(B -V) = 0.025 mag and E(B -V) = 0.32 mag are drawn for comparison. Compared to our previous inversion (A, at top) the structures are more compact for inversion D (at bottom), showing that the data are more consistent and the resolution is improved thanks to Gaia distances and additional data.

  Figure 8 is a zoom in on these 2D maps of A and D integrated color excesses, this time in a limited region of the sky close to the anticenter direction. Compared to our previous inversion, D leads to better agreement with the Schlegel et al. (1998) contours at low reddening, demonstrating that the inclusion of DIB-based data has improved the detection and location of the structures.

Fig. 13 .

 13 Fig. 13. Error in the distance due to the limited target space density, in the Galactic plane, as estimated from the radii of the smallest spheres that contain at least 10 targets (left figure) or from the target density in a cube of side 200 pc (right). The pale pink color corresponds to errors larger than 200 pc.

Fig. 14 .

 14 Fig. 14. Estimated error on the integrated color excess for the Galactic plane computed as the mean difference between data and model in a restricted volume (see Sect. 5.1). The pale pink color corresponds to errors larger than 0.4 mag.

  Schlafly et al. (2014) produced a catalog of molecular cloud distances based on Pan-STARRS photometric data. Most of the clouds are out of the Galactic plane, where the number of structures distributed along the sightline is relatively small (see Fig.

Fig. 15 .

 15 Fig. 15. Four vertical planar cuts in the 3D opacity maps in planes containing the ROSAT 3/4 keV X-ray bright spots labeled 1 to 4 in Puspitarini et al. (2014) and here as a) to d); the black dashed ellipses show the likely locations of the corresponding SNRs; the latitudinal grid is every 10 deg.

Fig. A. 1 .

 1 Fig. A.1. Vertical cut in the 3D opacity distribution for D inversion along the meridian plane l = 0-180 • . Color scale and iso-contours as in Fig. 9.

Fig. A. 2 .

 2 Fig. A.2. Same as Fig. A.1 for the vertical plane along l = 30-210 • .

Fig

  Fig. A.3. Same as Fig. A.1 for the vertical plane l = 60-240 • .

Chapter 5

 5 Conditions for DIB formation: a new view on the "skin effect" Based on: "Updated extraction of the APOGEE 1.5273 µm Near-Infrared Diffuse Interstellar Band: a Planck view on the carrier depletion in dense cores" Elyajouri, M. & Lallement, R. In revision, Astronomy & Astrophysics Journal. Abstract Context. Constraining the spatial distribution of Diffuse Interstellar Band (DIB) carriers and their links with gas and dust are mandatory steps in understanding their role in interstellar chemistry. Aims. The analysis of the latest SDSS/APOGEE data release DR14, detailed in Chapter 3 of Part. I, has led to the production of a new catalog of 124 064 DIBs, allowing us to revisit the correlation between DIB strength and dust reddening.

Figure 51

 51 Figure 51 The equivalent width EW of the 15 273 Å DIB as a function of stellar extinction A V . Black points are the individual sightline measurements. The red circles are the EW medians in each ∆A V = 1.6 mag, and the yellow line is the fitted EW/A V relationship. Superposed are the background stars of Barnard 68 (the larger blue circles).

Figure 52

 52 Figure 52 Spectrum of 2MASS J19420307+2317393 from ASPCAP (solid black) and Cannon (dashed line) data products. The associated synthetic stellar models are shown in dashed curves. We clearly see the difference between ASPCAP (blue) and Cannon (red) models around the 15 273 Å DIB location (black arrow). An offset was applied to ASPCAP data and model to facilitate the comparison.

5 .Figure 53

 553 Figure 53 Comparison between the DIB EWs derived in this work and those from the Zasowski et al. (2015) catalog. Both values are plotted as a function of the Cannon extinction value A V . Linear fits to both datasets (dashed blue and pink lines) are superimposed.

Fig. 56

 56 Fig. 56 is a map of the Planck optical depth τ 353 in the region of the nearby Orion clouds (data from Planck Collaboration et al. (2016)). Superimposed on the map are the APOGEE targets, with markers color-coded according to their EW/A V ratio. To ensure that the stars are located behind the Orion clouds, targets closer than 600 pc were eliminated. In addition, we discarded very low reddening targets and/or very low DIB equivalent widths in order to avoid the corresponding uncertain ratios. The corresponding thresholds are A V ≥ 0.2 mag and EW ≥ 0.03 Å. The DIB carrier disappearance in the dense phase is conspicuously illustrated in this figure: towards all cloud cores (τ 353 ≥ 10 -4 ) the DIB to extinction ratio decreases dramatically. Recip-

  Figure 57 Same as 56 for the Taurus region.

Figure 58

 58 Figure 58 Same as 56 for the Cepheus region.

Figure 59 DIB

 59 Figure 59 DIB Equivalent width to extinction A V ratio towards stars behind Taurus (top), Orion (middle) and Cepheus (bottom) cloud complexes, as a function of the dust optical thickness measured by Planck τ(353 GHz). Linear fits in the Log-Log scale are shown in red.

  Figure511The DIB to extinction ratio EW/A v as a function of τ 353 for all APOGEE sightlines with EW ≥ 0.03 Å and Av ≥ 0.2 mag. These limitations ensure a reliable determination of the EW/Av ratio. Orion, Taurus and Cepheus targets are marked by black signs, while all other targets are color-coded according to the Galactic latitude. Here, one can distinguish three different regimes: high-latitude, low dust sightlines that correspond to nearby diffuse clouds and a high EW/A v of ≃ 0.23 Å mag -1 , partially mixed phases in nearby off-Plane cloud complexes following the Taurus-Orion-Cepheus trend (red curve), and distant, high dust optical depth sightlines within the midplane for which the EW/A v ratio reaches a constant value of ≃ 0.056 Å mag -1 (blue dashed curve).

Figure 61

 61 Figure 61 λλ15617, 15653, 15673 NIR DIBs toward six TSS targets from the new catalog. Reduced APOGEE spectra are shown with solid color lines: 2MASS J03483498+5048039 (brown), 2MASS J03584538+5222502 (green), 2MASS J00165734+6333108 (red), 2MASS J04360336+3640031 (blue), 2MASS J00274417+6001430 (orange), and 2MASS J00281188+5905318 (pink). Spectra are in the stellar rest frame and vertically offset by -0.04, -0.02, 0., 0.02, 0.04, and 0.06 from bottom to top. The solid black curves represent the fitted model. The dot-dot-dashed green line is an example of an adjusted APOGEE stellar model. The black rectangles indicate the masked regions.

Figure 62 Figure 63

 6263 Figure 62 Histograms of the λλ15617, 15653, and 15673 DIB widths (FWHM) for the targets of the catalog.

Figure 64 Figure 65

 6465 Figure 64 Stacked spectra created from 164 sight lines selected for their strong, well detected λ15 273 DIB (solid black curve), and for 40 sightlines selected for a combination of high signal, clean spectrum, and weakness of the λ15 273 DIB (blue solid curve). Before stacking, the spectra have been shifted to a common rest frame. In each figure the pink dashed lines show the telluric models and the green dashed curve displays the NIR emission sky. At the location of an actual DIB we expect the black curve to exhibit a depression by comparison with the blue curve. Telluric absorption and emission spectra allow us to distinguish artifacts that are due to telluric lines and real interstellar absorptions. Weak NIR DIBs detected previously are indicated by colored boxes with the following code: green box: detected by Geballe et al. (2011) and confirmed by Cox et al. (2014); yellow box: detected by Geballe et al. (2011) , but not confirmed by Cox et al. (2014); red box: potential new detection.

Figure 67

 67 Figure 67 Correlation between the λλ15617, 15673, and 15653 NIR DIBs and the strong λ15 273 NIR DIB. The slope for the ODR proportional linear fit is shown in each plot.

Figure 68

 68 Figure68Representative fit examples for the optical DIBs for three stars observed with NARVAL (columns 1-3) and one star observed with SOPHIE (last column). They are ordered from bluer (top) to redder (bottom) band. Each panel contains a main graphic on top, with the observed spectrum in black and the fit in red. Integration limits for estimating the equivalent width are marked in cyan. The auxiliary graphic at the bottom contains the residuals in red.

Figure 69

 69 Figure 69 From top to bottom: correlation between the equivalent widths of the strongest NIR λ 15 273 DIB and of the five optical λλ5797, 6196, 6614, 5780, and 6283 DIBs. Compared to the Cox et al. (2014) targets, the OHP-SOPHIE and TBL-NARVAL targets correspond to shorter sight line and weaker absorptions. For the λ6283 DIB only the latter two datasets are presented. Correlation coefficients r and reduced χ2 resulting from the linear ODR fit are indicated in each plot.

Figure 610

 610 Figure 610 Sensitivity of the λ15 273 DIB to the radiative environment: ratios of λ15 273 EWs with five optical DIBs are shown as a function of the radiation-sensitive ratio EW(5797)/EW(5780). The right scale range is chosen from zero to three times the average ratio. An ODR linear fit provides the relative slopes indicated in the figure along with the correlation coefficients. The largest slope is found for λ5797 band and the smallest for the λ6283 DIB. Here, we show (red line) an exponential fit to the whole dataset and a linear fit (black dashed line).

Fig. 5 .

 5 Fig. 5. a) Part of the extracted spectrum of HD 23180 taken using the Red Lower EEV CCD (Red-L). b) Ditto but for Red Upper MIT CCD (Red-U). The latter is a thick chip so fringing is much reduced compared with the EEV detector. The vertical scale is the same in both cases.

Fig. 6 .

 6 Fig.6. Close-up view of a region including two overlapping orders in the 564-nm setting for HD 23180 for both reduction "A" (bottom red trace) and reduction "B" (top black trace). The small jump in the continuum at approximately 5855 Å seen in reduction B (top black trace) is due to imperfect merging of two echelle orders. The apparent difference in S/N is due to alternative choices of wavelength sampling.

Fig. 7 .

 7 Fig. 7. Comparison between the ADP and EDIBLES processing of HD 184915 spectra (0.02 Å binsize). The S/N measurements, taken at 4912-4913 Å and 6129-6130 Å for the Red-L and Red-U spectra, are given in the top and bottom panels. Resampling to 0.04 Å binsize (i.e. corresponding to the spectral resolution) further increases the S/N by factor √ 2.

Fig. 8 .Fig. 9 .

 89 Fig. 8. EDIBLES UVES spectra of HD 170740 (B2 V) for each setting from top to bottom: 346B, 437B, 564L, 564U, 860L, 860U. This overview figure is a demonstration of the data quality. The main gaps in wavelength coverage are between 5610-5670 Å and 8530-8680 Å which correspond to the physical separation of the Red-L and Red-U detectors in both the 564 and 860-nm settings. Note also the inter-order gaps, several are indicated with arrows, in the 860-nm Red-U spectrum above ∼9600 Å as well as several conspicuous regions containing bands of closely-spaced telluric absorption lines (indicated with red horizontal bars) mostly in the Red-L and Red-U 860-nm spectra (bottom two panels). Two ordermerging jumps are indicated in the fourth panel. A more detailed version of this figure is shown in Fig. B.1 where specific interstellar species are labeled and a synthetic DIB spectrum is shown for comparison.

A76, page 10 of 29 N

 29 . L. J.Cox et al.: The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES). I.

Figure 91

 91 Figure 91 Illustration of global analysis of the NaI 3302.368 and 3202.987 Å UV doublet (NaIB; left), the NaI 5889.95/5895.92 Å D 2 -D 1 doublet (NaI; middle) and the K and H Ca II lines at 3933.66 and 3968.47 Å (Ca II; right) for HD23180. Left axis is flux in arbitrary unit and the bottom axis is heliocentric velocity (km s -1 ). Data are shown with marked black lines. The red line shows the global fit. The blue dashed line shows the voigt profile for each component (not shown for Ca II). The different red dots show the successive initial guesses for the location of each additional cloud.

C 2 -Figure 101

 2101 Figure 101 NaI doublet at 3302.368/3202.987 Å (left), and NaI D1-D2 and the stellar HeII (right) line are shown for the seven selected single-cloud lineof-sight (one main cloud component) and the multi-cloud target HD 169454. The NaI spectra have been shifted to their air rest wavelengths. The same velocity shift has been applied to the NaI D1-D2 lines. The applied color coding is used in all other figures as well.

  spectra in the range 8750-8785 Å were normal-203 10. Characterization of DIB profiles

  Figure102Observed Phillips (2-0) C 2 bands and their best-fitting model profiles. The R 10 and R 2 lines at 8753.949 and 8753.578 Å have not been included in the fit because of the presence of a CCD artifact near these wavelengths that could not be corrected by flat-fielding. In some cases, Q(4) line is blended with a suspected DIB (or stellar line).

Figure 108 Figure 109

 108109 Figure 108 Extracted average C 2 -DIB profiles (i.e., from stacking profiles of HD 23180, HD 203532, and HD 147889) normalized to the same depth of 4963 Å DIB. Tick marks refer to the location of sub-peaks of Table104.

Figure 1010

 1010 Figure 1010 Normalized spectral intervals between the sub-peaks in the profiles of several C 2 -DIBs as a function of the C 2 -based kinetic temperature (see text) of each target. Uncertainties on the intervals can be deduced visually from the drawn profiles in Figs. 103, 104, 105, 106, and 107, and are on the order of the data point dispersion. The dashed line is a linear fit to the average over the five different DIBs. The size of the marker indicates the reliability of the sub-peak interval determination, again visually determined from the quoted figures. The three targets used for the co-addition of the DIB profiles are marked by arrows.

Figure 1011

 1011 Figure 1011 Comparison between C 2 DIB profiles normalized to their central depths toward HD 23180 (blue), HD 203532 (green), HD 147889 (black) and HD 169454 (brown). The first three targets are those chosen for profile coadditions, for which results are shown in previous figures. The C 2 excitation temperatures are 20, 23 and 49K for HD 23180, HD 169454 and HD 147889 respectively (see Table102).

Figure 1012

 1012 Figure 1011 Comparison between C 2 DIB profiles normalized to their central depths toward HD 23180 (blue), HD 203532 (green), HD 147889 (black) and HD 169454 (brown). The first three targets are those chosen for profile coadditions, for which results are shown in previous figures. The C 2 excitation temperatures are 20, 23 and 49K for HD 23180, HD 169454 and HD 147889 respectively (see Table102).

Figure 1013

 1013 Figure 1013 Same as Fig. 1011 but for 4363, 5769, 5175, 5546 and 6729 Å.

Figure 1014 Figure 15

 101415 Figure1014Correlations between the DIB equivalent widths and the C 2 column (green filled circles). Superimposed as black squares are the results of[START_REF] Galazutdinov | On the diffuse bands related to the C 2 interstellar molecule[END_REF]. R is the Pearson coefficient for our data only. The multi-cloud target HD 169454 value corresponds to N(C 2 )= 0.71 × 10 14 cm-2 221

Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Figure 23

 1617181920212223 Figure 16 The 5780 DIB recorded along seven single-cloud and one multicloud line-of-sight. Top in all panels: normalized average profile. Middle in all panels: overlaid DIB profiles at the same vertical scale for all stars. Bottom in all panels: same DIB vertically displaced and depth-equalized profiles. The black dashed line shows the DIB location. 226

Figure 1

 1 Figure 1 λλ15617, 15653, 15673 NIR DIBs toward APOGEE TSS targets. APOGEE spectra are shown with solid black lines. The initial synthetic models are shown in blue. The dot-dot-dashed green lines display the adjusted APOGEE stellar model. The solid magenta curves represent the fitted model. The gray rectangles indicate the masked regions.

Figure 2

 2 Figure 2 Same as Fig. 1.

Figure 3

 3 Figure 3 Same as Fig. 1.

Figure 4

 4 Figure 4 Same as Fig. 1.

Figure 5

 5 Figure 5 Same as Fig. 1.

Figure 1 Figure 2

 12 Figure 1 Examples of fit. In each figure: The blue curve is the convolved stellar model. The magenta curve is the fitted stellar+telluric model. The dashed orange line shows the DIB position. Residuals are shown in red. 248

Figure 3

 3 Figure 3 Same as Fig. 1.

Figure 4

 4 Figure 4 Same as Fig. 1.

Figure 5 Figure 1

 51 Figure 5 Same as Fig. 1.

Figure 13

 13 Figure 13 Same as Fig. 10 but for HD93222, HD93843, HD99953, HD103779, HD104705, HD109399, HD111934, and HD113904.

Figure 17

 17 Figure 17 Same as Fig. 10 but for HD184915, HD185418, HD185859, HD186745, HD186841, HD203532, and HD303308.

Figure 20

 20 Figure 20 Illustrations of the multi-cloud automated fitting of the CaII K-H interstellar lines. Marked black lines show the observed data. The red lines display the fit.

• Part III, Chapter 6 -Exploitation of the catalog of the strongest NIR DIB for weak DIB extractions and studies of the response to ioniza- tion

  In this chapter I present the results of the massive stacking of APOGEE spectra selected for strength or on the contrary the absence of the strong 15 273 Å DIB. This has made it possible to extract and characterize weak DIBs that were previously detected in only a few lines of sight and to detect new candidates. DIB-DIB ratios were studied, including the first exploration of ratios involving a NIR DIB and a DIB in the visible range. The optical data were obtained from dedicated observations with Sophie at OHP/1.93m and NARVAL at Pic du Midi/TBL.

The DIB-DIB ratios contain information on the evolution of the carriers according to physical properties, i.e. radiation field, ISM abundances and particle size distribution. A hierarchy of DIBs ordered by sensitivity to the ionizing field is currently being carried out thanks to the work of several teams (see e.g.

Ensor et al. 2017a

) and some of the work done in my thesis has contributed to improving this hierarchy by scaling up the DIB 15 273 Å.

  2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE Hot Telluric Calibrators absorption characteristics are applied to the results. In particular limiting constraints on the DIB widths and Doppler shifts are deduced from the HI 21 cm measurements, following a new technique of decomposition of the emission spectra. From ∼16 000 available hot telluric spectra we have extracted ∼ 6700 DIB measurements and their associated uncertainties. The statistical properties of the extracted absorptions are examined and our selection criteria are shown to provide a robust dataset. The resulting catalog contains the DIB total equivalent widths, central wavelengths and widths.

  2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE Hot Telluric Calibrators the DIB equivalent width (EW) is the area of the fitted Gaussian and can be simply analytically derived from its parameters as the product of the model DIB's depth D by the coefficient σ:

1 10 100 1000 Number per bin -400 -200 0 200 400 Velocity (km.s -1 )

  2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE Hot Telluric Calibrators

	2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE
	Hot Telluric Calibrators
		0.10	All telluric	50%
			Detected DIBs (6354)
					30%
						350
		0.08
						300
	Error on EW DIB	0.06 0.04				250 200 150	Galact. long
						100
						50
		0.02
						0
		0.00
		0.0	0.1	0.2	0.3	0.4
				EW DIB (Å)
	Figure 23 Estimated error on the DIB equivalent width EW for all the targets
	of the catalog. Above (resp. below) dashed lines relative errors are larger
	(resp. smaller) than 30 and 50 %. The color scale refers to the Galactic longi-
	tude of the target star. Due to our method, relative errors are overestimated
	for targets at lower longitudes (violet, dark blue signs, see text).
	standard deviation on the residuals. This comparison was done in a one-or
	two-step process. We first compare the absorption depth with the maximum
	of the three standard deviations R A , R B and R C described in subsection
	2.3.2. If it is larger (arrow (d)), then start directly the tests on the width.
	If the depth is smaller that this maximum, i.e if the spectrum is globally
	too noisy to detect the DIB (the arrow (c)) or the DIB is very shallow, we
	also compared the absorption depth with the local standard deviation R A
	only. This second step allows to detect those spectra that are noisy in those
	spectral ranges far from the DIB but that have a good enough signal to noise
	in the DIB neighborhood to ensure a detection, i.e. the use of the maximum
	of the three R A , R B and R C deviations is too restrictive. Absorptions that
	pass successfully the test (arrow (e)) are then examined for their widths in
	the same way than the previous ones (arrow(d)) but are flagged differently.
	The difference in the flags serves to evaluate the quality of the spectrum and

1.01 1.00 0.99 0.98 0.97 0.96 0.95 0.94 Normalized Flux 15290 15285

						2MASS J00013391+5904490
	15250	15255	15260	15265	15270	15275	15280
				Wavelength (Å)
	Figure 26 Example of detection of narrow DIB (0.7 Å < σ < 1 Å): 2MASS
	J00013391+5904490 with σ = 0.9 ± 0.1 Å, λ c = 15272.2 ± 0.1 Å and EW = 0.041 ± 0.021 Å. Color and symbol code is as in Figure

.01 1.00 0.99 0.98 0.97 0.96 0.95 0.94 Normalized Flux 15290 15285 15280 15275 15270 15265 15260 15255 15250 Wavelength (Å) 2MASS J07054216+0146062 1.01 1.00 0.99 0.98 0.97 0.96 0.95 0.94 Normalized Flux 15290 15285 15280 15275 15270 15265 15260 15255 15250 Wavelength (Å) 2MASS J16295254-0108360 1.01 1.00 0.99 0.98 0.97 0.96 0.95 0.94 Normalized Flux 15290 15285 15280 15275 15270 15265 15260 15255 15250 Wavelength (Å) 2MASS J03435284+2517060 1.01 1.00 0.99 0.98 0.97 0.96 0.95 0.94 Normalized Flux 15290 15285 15280 15275 15270 15265 15260 15255 15250 Wavelength (Å)

	2. A catalog of 1.5273 µm Diffuse Interstellar Bands based on APOGEE
	Hot Telluric Calibrators
		1.00					2MASS J06432204+0934245	1.00				2MASS J17330344-2939030
	Normalized Flux	0.98 0.96 0.94								Normalized Flux	0.98 0.96 0.94	0°90°180°270°0
		0.92									0.92
		0.90 15250	15255	15260	15265	15270	15275	15280 0.02	15285	15290 0.04	0.06 0.90 15250	0.08 15260 EWDIB (Å) 15255	0.10 15265	15270	0.12 15275	15280	.14 15285	15290
					Wavelength (Å)						Wavelength (Å)
		12MASS J04363913+4104368
	Figure 28 Illustration of the various categories of selected absorptions: (Top
	left) Detected-flag7; (Top right) Recovered-flag6; (Center left) Narrow-flag5;
	(Center right) Recovered Narrow DIBs -flag4; (Bottom left) Stars from plate
	4529 -flag3; (Bottom right) Be Stars -flag2. Color and symbol code is as in
	Figure 22.

Table 22 .

 22 Sample stars with 15 273 Å DIB detection.

	2MASS ID	GLON	GLAT	λ c ± err(λ)	σ ± err(σ)	EW ± err(EW)	Flag	EW H I 21cm
	00001687 + 5903034 116.390 -03.169 15274.0 ± 0.2 00001877 + 5938132 116.500 -02.595 15272.3 ± 0.1 00005414 + 5522241 115.740 -06.791 15271.1 ± 0.3 00010331 + 1549325 105.570 -45.340 15272.0 ± 0.4 00010512 + 5651581 116.060 -05.332 15271.9 ± 0.4 00011569 + 6314329 117.320 +00.919 15274.5 ± 0.1 00013391 + 5904490 116.550 -03.172 15272.2 ± 0.1 00013919 + 7005554 118.690 +07.639 15272.8 ± 0.2 00022269 + 6254032 117.380 +00.559 15273.5 ± 0.1 00023939 + 7109185 118.970 +08.659 15271.7 ± 0.3 00024298 + 5748303 116.460 -04.450 15272.4 ± 0.2 00024671 + 1605384 106.240 -45.194 15272.8 ± 0.2 00024931 + 5709106 116.350 -05.096 15272.7 ± 0.3 00024931 + 5709106 116.350 -05.096 15272.7 ± 0.2 00025231 + 5841372 116.650 -03.584 15272.9 ± 0.2 00025606 + 7014169 118.820 +07.754 15273.2 ± 0.3 00032397 + 7310282 119.420 +10.631 15268.4 ± 0.5 00042334 + 5626552 116.430 -05.828 15273.0 ± 0.2 00044189 + 6939114 118.860 +07.152 15274.4 ± 0.3 00044627 + 0132128 099.672 -59.211 15271.4 ± 0.6 00045177 + 5933503 117.060 -02.775 15272.5 ± 0.4 00045306 + 7048114 119.090 +08.280 15273.6 ± 0.8 00050083 + 6349330 117.840 +01.414 15271.9 ± 0.1 00051293 + 5811530 116.860 -04.127 15272.7 ± 0.1 00051873 + 5758464 116.830 -04.345 15274.0 ± 0.2 00052394 + 6347207 117.870 +01.370 15271.9 ± 0.1 00052888 + 5717190 116.730 -05.028 15272.0 ± 0.3 00052965 + 5722141 116.750 -04.948 15272.3 ± 0.3 00053673 + 7530446 120.010 +12.901 15272.2 ± 0.2 00054538 + 6234237 117.700 +00.167 15274.7 ± 0.1 00060064 + 7001540 119.040 +07.504 15271.3 ± 0.3 00060945 + 6412287 118.030 +01.768 15272.1 ± 0.1 00061415 + 0127198 100.290 -59.414 15271.8 ± 0.2 00063033 + 5719168 116.870 -05.021 15272.8 ± 0.2 00064418 + 7541165 120.110 +13.062 15271.0 ± 0.4 00064478 + 0025574 099.822 -60.418 15271.6 ± 0.2 00064971 + 5737102 116.970 -04.734 15272.7 ± 0.4 00065832 + 5726544 116.960 -04.906 15272.2 ± 0.4 00071354 + 5804306 117.100 -04.295 15272.8 ± 0.2 00072008 + 6241381 117.900 +00.254 15272.9 ± 0.1 00074256 + 6318026 118.040 +00.845 15271.3 ± 0.1 00074657 + 5722435 117.050 -04.993 15272.0 ± 0.2 00074863 + 7040220 119.310 +08.109 15271.7 ± 0.6 00080292 + 7332356 119.820 +10.934 15271.8 ± 0.2 00081304 + 6315383 118.090 +00.796 15273.9 ± 0.1 00085840 -0015231 100.410 -61.257 15273.3 ± 2.5 00090905 + 5737124 117.270 -04.786 15271.8 ± 0.2 00091817 + 7022314 119.380 +07.795 15272.6 ± 0.2 00092728 + 7447408 120.130 +12.152 15271.4 ± 0.2 00094423 + 0117063 101.810 -59.865 15272.6 ± 0.4	1.1 ± 0.2 1.4 ± 0.1 1.9 ± 0.3 0.8 ± 0.5 1.4 ± 0.4 1.5 ± 0.2 0.9 ± 0.1 1.6 ± 0.2 2.0 ± 0.2 1.7 ± 0.3 1.2 ± 0.2 0.7 ± 0.2 1.7 ± 0.3 1.3 ± 0.2 1.8 ± 0.2 1.2 ± 0.3 0.8 ± 0.6 0.8 ± 0.2 0.9 ± 0.3 0.9 ± 0.6 2.2 ± 0.4 1.4 ± 0.8 2.0 ± 0.1 1.2 ± 0.1 1.6 ± 0.2 1.7 ± 0.1 1.2 ± 0.3 1.4 ± 0.3 1.7 ± 0.2 1.4 ± 0.1 1.4 ± 0.3 1.7 ± 0.1 0.8 ± 0.2 0.8 ± 0.2 2.3 ± 0.4 0.7 ± 0.2 2.1 ± 0.5 1.8 ± 0.4 1.2 ± 0.2 1.4 ± 0.1 1.4 ± 0.1 1.4 ± 0.3 0.9 ± 0.6 0.9 ± 0.2 1.3 ± 0.1 0.9 ± 2.7 0.9 ± 0.2 1.2 ± 0.2 0.9 ± 0.2 1.7 ± 0.5	041 ± 018 075 ± 019 058 ± 018 010 ± 021 027 ± 014 097 ± 020 042 ± 022 062 ± 011 128 ± 025 053 ± 010 033 ± 016 016 ± 012 049 ± 013 058 ± 017 069 ± 017 029 ± 013 008 ± 016 024 ± 022 021 ± 013 008 ± 009 066 ± 027 014 ± 007 175 ± 030 060 ± 013 070 ± 024 135 ± 019 026 ± 014 043 ± 024 082 ± 023 133 ± 029 036 ± 011 130 ± 023 023 ± 016 021 ± 015 071 ± 026 024 ± 017 048 ± 016 040 ± 017 041 ± 023 139 ± 033 120 ± 022 043 ± 014 009 ± 010 025 ± 022 085 ± 023 002 ± 014 036 ± 015 038 ± 011 038 ± 012 033 ± 014	7 7 7 1 6 7 5 7 7 7 7 5 6 7 7 7 1 4 4 1 7 6 7 7 7 7 7 7 7 7 7 7 5 5 6 4 7 7 7 7 7 7 4 3 7 1 5 7 5 7

  3. A catalog of 1.5273 µm Diffuse Interstellar Bands based on late-type stars

			2M00073384+6843500			2M00002853+6119307		2M00095633+6739200
		1.1					1.1					1.1
	Normalized Flux	1.0 0.9				Normalized Flux	1.0 0.9				Normalized Flux	1.0 0.9
		0.8
		0.7					0.8			Data Stellar model Global fit DIB model	0.8
												0.7
		15260	15265	15270	15275	15280	15265	15270	15275	15280	15285	15270	15275	15280	15285	15290
				Wavelength (Å)			Wavelength (Å)				Wavelength (Å)
	Figure 31 Illustration of DIB extraction towards three red giants stars. In
	each figure: APOGEE spectra are shown with solid black curves. The initial
	stellar model provided by APOGEE Cannon is shown in solid blue. The
	DIB absorption alone is represented in orange (with an offset). The solid
	magenta curves represent the final stellar+DIB modeled spectrum.

1.2 1.1 1.0 0.9 0.8 0.7 0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual 2M00022406+1625382 1.2 1.1 1.0 0.9 0.8 0.7 0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual 2M12560496+4211353 1.2 1.1 1.0 0.9 0.8 0.7 0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual

	3. A catalog of 1.5273 µm Diffuse Interstellar Bands based on late-type
	stars
	1.2
	1.1
	1.0
	0.9
	0.8
	0.7
	2M13063740-0156384
	Figure 32 Illustration of DIB upper limit derivation, in the case of non con-
	vergence of the initial fitting. A Gaussian DIB profile with the maximum
	equivalent width compatible with the standard deviation in the data-stellar
	model residuals is shown at an imposed wavelength of 15272.42 Å in solid
	black (with an offset).

0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual 2M06443977+4448257 1.2 1.1 1.0 0.9 0.8 0.7 0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual 2M00011207+5704332 1.2 1.1 1.0 0.9 0.8 0.7 0.6 Normalized Flux 15300 15290 15280 15270 15260 15250 Wavelength (Å) -0.05 0.00 0.05 Residual 2M17425909-0637043

  Figure 33 Illustration of DIB extraction in the case of convergence following the derivation of an optimal DIB prior (the initial guess based on the preliminary computation of data-stellar model residuals and its Gaussian fit). The fitted DIB is shown in dashed orange with an offset. 3. A catalog of 1.5273 µm Diffuse Interstellar Bands based on late-type stars

Table 1 .

 1 Fitting constraints of λ15273 NIR DIB for the selected APOGEE spectra.

  4.4. ConclusionVergely et al. 2010; Lallement et al. 2014; Sale, Magorrian 2014). A strong advantage of NIR DIBs is the potential use of highly reddened target stars and the exploration of dense IS clouds. Moreover, nowadays instrumentation is becoming more and more NIR-IR oriented and large datasets in this spectral range are foreseen. 3D maps of the Galactic ISM are tools of wide use and their quality is expected to increase considerably in future now that the Gaia distances are available. Capitanio et al. (2017) presented such 3D maps from composite data. Finally, DIB-based maps can be compared with dust and gas 3D distributions, providing new diagnostics of the conditions of their formation, ionization and destruction. They can also help interpreting small-structure of ISM clouds and of UV radiation impact on DIBs revealed by high spatial or temporal resolution spectroscopic data(Cordiner et al. 2013; Smith et al. 2013).

  7 for the Barnard 68 dark cloud. The corresponding DIB EW data points correspond to some of the largest departures from the values predicted by the linear relation. However, they are still comparable to tens of other similar cases, suggesting that the marked disappearance of DIBs in Barnard 68 is far from an exception. 99 5. Conditions for DIB formation: a new view on the "skin effect" Figure 55 Cannon and RJCE extinction estimates for target stars from Table1fromZasowski et al. (2015). The RJCE estimate used by these authors and listed in their table is ≃ 25% higher than the Cannon estimate used in the present work.

		10	a=-0.11 +/-0.38 b=1.26 +/-0.10
		8
	Av (RJCE)	6
		4
		2
		0
		0	2	4	6	8
			Av (Cannon)

  In addition, a catalog of λ15 273 measurements based on the TSSs has been presented in 2. Here we focus on the three other, weaker bands. Based on the few available detections[START_REF] Geballe | Infrared diffuse interstellar bands in the Galactic Centre region[END_REF] Cox et al. 2014), we expect them to be between two and three times fainter than the band at λ15 273. The first part of this chapter aims at creating a catalog of equivalent widths and central wavelengths for these three strongest-weak DIBs. Our previous measurements of the strong λ15 273 DIB serve as a reference for wavelength shifts and enter further correlation studies.

	6.3 Catalog of λλ15617, 15653, and 15673 DIBs
	Based on earlier results by Cox et al. (2014), it appears that only four DIBs
	satisfy EW/FWHM ≥ 3: λλ15 273, 15617, 15653, and 15673 bands in the 15100.08 to 16999.8 Å range. The λ15 273 DIB is by far the strongest interstel-
	lar band in this spectral range and has been extensively studied by Zasowski
	et al. (2015).
	and the rope length
	method described in Raimond et al. (2012).

Table 61

 61

	Fitting constraints of λλ15617,15653, and 15673 NIR DIBs.
	spectral range σ max	depth
	( Å)	( Å)
	15617 [15607,15620] 15653 [15648,15660] 15673	5 7	≥ 0.0001 ≥ 0.0001

Table 62

 62 Basic properties of λλ15617,15653, and 15673 NIR DIBs.

		λc ( Å)	mean FWHM Peak FWHM EW/E(B -V) (*) No. of successful fits ( Å) ( Å) (m Å /mag)
	15617 15653 15673 (*) based on the λ15 273 DIB	15616.13 ± 0.07 15651.38 ± 0.07 15671.82 ± 0.03	4.37 5.72 3.74	4.03 4.56 3.31	51 77 96	295 262 308

Table 63 DIB

 63 detections in the APOGEE spectral range (except for the strong λ15 273 DIB) and their widths.

	λ DIB	Geballe11(*)	Cox14(**)	This work	This work
	( Å)	FWHM( Å)			mean FWHM ( Å)
	not confirmed 295 LOS 262 LOS 308 LOS not confirmed confirmed 160 stacked spectra 15225 30 ± 10 (4 LOS) not confirmed 3 LOS 15617 10 ± 2 (5 LOS) 2 LOS 15653 15 ± 4 (6 LOS) 15673 3 LOS 9 ± 2 (6 LOS) 15990 9 ± 2 (4 LOS) 1 LOS confirmed (idem) 16232 24 ± 3 (6 LOS) 16573 (4 LOS) 1 LOS confirmed (idem)	-4.37 5.72 3.74 5.4 17:: 5.2
	16585	(6 LOS)	2 LOS	confirmed (idem)	3.3
	16596	(5 LOS)	1 LOS	not confirmed	-
	15235	-	-	new candidate?	-
	16769	-	-	new candidate	2.8

(*) Geballe et al, 2011, (**) Cox et al, 2014, (::) uncertain, LOS : line of sight extracted the DIB profile. The continuum-normalized spectra are shown in Figs. 63 and 65. Gaussian fits to the profiles provided the band widths listed in Table

Table 64 .

 64 Extracted equivalent widths for optical DIBs.

	2MASS ID	EW 5780	EW 5797	EW 6196	EW 6283	EW 6614

Table 65 :

 65 Equivalent widths and central wavelengths of the λλ15617, 15653, and 15673 DIBs. The wavelengths are in the APOGEE stellar frame and can be converted into heliocentric wavelengths using the stellar radial velocity listed in the last column. This table is published in its entirety in electronic form at the CDS via anonymous ftp to cdsarc.ustrasbg.fr(130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/ qcat?J/A+A/.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J00052394+6347207 J00162429+6329460 J00165734+6333108 J00194084+6148481 J00213429+5855479 J00220340+6211063 J00231234+6410322 J00274417+6001430 J00281188+5905318 J00283015+6341424 J00284518+6239253 J00285254+5527583 J00300848+6246456 J00311664+6220376 J00313144+6010201 J00313306+6004195 J00350607+6258585 J00535925+6000485 J00544879+6754306	117.87 119.04 119.10 119.19 119.08 119.51 119.86 119.97 119.95 120.39 120.33 119.71 120.50 120.59 120.46 120.45 121.08 123.25 123.25	+1.371 +0.890 +0.938 -0.827 -3.718 -0.491 +1.472 -2.707 -3.645 +0.937 -0.100 -7.264 +0.008 -0.436 -2.604 -2.704 +0.170 -2.857 +5.039	12± 14 43± 28 27± 23 11± 10 25± 24 24± 67 14± 15 27± 22 16± 18 36± 28 19± 17 9± 14 26± 20 9± 9 19± 22 15± 23 36± 24 29± 39 21± 20	56± 31 69± 45 34± 21 41± 21 19± 19 36± 55 33± 18 27± 19 18± 14 14± 18 12± 35 16± 15 33± 24 34± 19 10± 13 29± 26 27± 21 36± 26 56± 43	40±13 47±25 16±11 27±15 30±15 45±14 25±12 38±11 24±13 24±14 62±21 16±10 65±22 34±17 33±15 38±17 46±18 73±20 18±13	15615.7± 0.5 15615.2± 0.6 15615.8± 1.1 15615.9± 0.5 15615.7± 0.9 15616.7± 0.6 15615.8± 0.8 15617.2± 1.1 15615.4± 1.0 15616.7± 0.8 15618.2± 0.6 15617.5± 1.5 15618.6± 0.5 15616.2± 0.4 15615.4± 0.8 15617.0± 1.0 15616.7± 0.8 15618.2± 1.0 15614.6± 0.6	15651.2± 1.0 15651.0± 1.1 15651.2± 0.8 15652.0± 0.8 15649.9± 0.9 15650.3± 0.7 15652.1± 0.8 15650.8± 0.9 15651.6± 0.6 15651.0± 1.1 15655.9± 4.1 15651.7± 1.2 15652.8± 0.7 15652.4± 0.5 15652.2± 0.6 15651.3± 0.8 15652.5± 0.7 15653.9± 0.8 15652.8± 0.9	15670.9± 0.2 15671.2± 0.4 15670.9± 0.6 15671.6± 0.4 15670.9± 0.4 15672.5± 0.3 15672.0± 0.4 15671.9± 0.2 15671.3± 0.4 15671.2± 0.4 15673.4± 0.3 15671.7± 0.6 15673.7± 0.3 15671.9± 0.3 15671.7± 0.3 15671.4± 0.3 15673.4± 0.3 15673.7± 0.2 15670.6± 0.3	-1.37 -3.45 -2.92 -14.22 +8.12 -25.46 -8.62 -13.88 -4.74 -3.18 -45.38 -25.34 -54.10 -16.89 -11.16 -4.13 -41.59 -62.34 +10.96
	J01201274+5839597 J01264564+6309556 J01300606+6334567 J01335381+5828560 J01540050+5801539 J01581986+5757166 J02220580+6040337 J02301257+6156001 J02364097+5553255	126.67 126.91 127.22 128.47 131.16 131.73 133.81 134.27 137.36	-3.996 +0.567 +1.033 -3.932 -3.837 -3.769 -0.266 +1.256 -4.005	45± 35 37± 29 44± 27 19± 31 12± 16 18± 24 10± 17 74± 46 60± 38	25± 32 34± 24 92± 51 13± 11 20± 16 39± 35 13± 13 85± 55 30± 24	30±17 63±26 48±19 31±13 62±21 69±27 27±20 84±34 55±24	15615.9± 0.4 15615.8± 0.4 15616.5± 0.4 15617.8± 1.5 15615.3± 0.5 15615.8± 0.7 15615.3± 0.6 15615.2± 0.4 15612.0± 0.5	15651.3± 1.8 15650.7± 0.4 15651.7± 0.9 15649.8± 0.4 15650.2± 0.4 15651.7± 1.9 15651.5± 0.4 15651.4± 0.7 15650.6± 0.4	15671.1± 0.2 15671.1± 0.2 15672.5± 0.3 15671.0± 0.3 15670.8± 0.2 15672.2± 0.3 15671.3± 0.4 15671.9± 0.2 15671.4± 0.2	-3.36 -3.34 -27.22 +6.41 +1.14 -15.37 +2.66 -12.83 -2.16

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J02441271+5639272 J02570063+6714204 J03123883+6303146 J03143063+5410503 J03163081+5745428 J03230976+5213502 J03233011+5208545 J03250988+5811061 J03251966+5650252 J03254141+4609481 J03302753+4708196 J03322659+5203572 J03403032+4935404 J03421355+4954547 J03431661+4958404 J03441133+5001177 J03453915+3226242 J03461161+4759007 J03483498+5048039 J03510857+3937512 J03572191+4701456 J03582144+5228184 J03584538+5222502 J04011751+5310145	138.00 134.65 138.16 142.96 141.33 145.11 145.19 142.06 142.83 148.84 148.96 146.38 148.87 148.90 148.99 149.08 160.49 150.60 149.15 156.61 152.67 149.26 149.37 149.14	-2.878 +7.271 +4.436 -3.030 +0.167 -3.985 -4.026 +1.143 +0.035 -8.805 -7.537 -3.321 -4.570 -4.147 -3.994 -3.871 -17.443 -5.278 -2.825 -11.213 -4.823 -0.572 -0.602 +0.247	35± 18 47± 27 21± 20 61± 45 49± 38 59± 38 59± 35 22± 18 16± 14 16± 14 30± 25 6± 9 14± 30 38± 27 18± 18 15± 18 33± 18 13± 66 28± 20 46± 34 45± 38 29± 22 21± 20 14± 18	58± 31 45± 25 32± 22 31± 34 47± 27 44± 39 62± 54 59± 39 . . . ± . . . 30± 20 13± 12 30± 25 16± 18 17± 12 22± 17 21± 23 60± 31 . . . ± . . . 37± 23 13± 17 40± 38 22± 20 48± 22 25± 25	56±16 49±24 26±20 45±18 48±13 60±26 63±18 48±18 37±17 36±12 33±12 37±17 24±11 61±17 36±12 19±12 37±14 43±22 34±14 55±28 55±40 32±12 44±14 31±13	15616.7± 0.3 15616.2± 0.5 15615.8± 0.5 15616.6± 0.5 15615.2± 0.6 15615.4± 0.5 15616.2± 0.5 15615.8± 0.5 15616.3± 0.3 15615.6± 0.6 15613.2± 1.0 15614.7± 0.4 15617.4± 1.1 15615.5± 0.8 15616.2± 0.7 15615.5± 0.7 15615.9± 0.7 15616.9± 0.6 15615.8± 0.6 15616.0± 0.6 15615.8± 0.4 15614.2± 0.7 15616.9± 0.7 15615.8± 0.9	15651.3± 0.5 15651.8± 0.6 15650.7± 0.5 15650.9± 1.4 15652.1± 1.0 15650.6± 1.0 15648.6± 1.1 15651.9± 0.8 15652.9± 0.3 15651.2± 0.9 15650.0± 0.6 15651.9± 0.6 15651.3± 0.7 15651.1± 0.5 15652.5± 0.6 15651.8± 1.1 15652.9± 1.1 . . . ± . . . 15650.9± 0.6 15650.8± 1.0 15649.8± 0.5 15649.3± 1.0 15651.9± 0.7 15650.5± 2.6	15671.9± 0.1 15672.2± 0.3 15672.3± 0.6 15671.6± 0.2 15671.9± 0.1 15672.1± 0.2 15671.8± 0.2 15671.5± 0.3 15671.8± 0.3 15671.0± 0.2 15670.6± 0.3 15671.5± 0.2 15671.8± 0.3 15671.9± 0.2 15672.4± 0.3 15671.5± 0.3 15672.5± 0.5 15672.7± 0.4 15671.5± 0.3 15672.6± 0.4 15672.3± 0.3 15670.7± 0.3 15672.0± 0.2 15671.1± 0.3	-11.43 -18.21 -2.46 +2.83 -16.36 -10.48 -2.22 -6.43 -10.40 +3.46 +18.27 +1.21 -4.79 -7.86 -14.25 +2.57 +11.27 -13.12 -0.32 -13.38 -13.35 +24.78 -6.23 +8.02
	J04022000+5323350 J04052313+5308558	149.12 149.62	+0.517 +0.636	17± 14 14± 16	50± 29 33± 24	37±15 23±11	15615.6± 0.6 15615.7± 0.9	15651.6± 1.3 15650.6± 2.0	15672.0± 0.4 15671.3± 0.4	-11.13 +12.75
	J04201999+4541378 J04213145+5617014 J04221174+5552136 J04260563+5530479 J04292916+3727399 J04295340+5555169 J04360336+3640031 J04362198+4800386	156.50 149.16 149.52 150.16 163.63 150.23 165.11 156.72	-3.124 +4.514 +4.289 +4.431 -7.630 +5.100 -7.198 +0.457	24± 36 43± 26 30± 23 39± 20 30± 28 19± 19 38± 20 5± 11	21± 37 37± 19 44± 25 111± 57 25± 24 22± 17 30± 18 33± 23	26±20 44±15 40±15 56±22 38±16 33±19 39±11 23±16	15615.4± 0.6 15617.9± 0.6 15616.3± 0.6 15617.2± 0.5 15618.0± 0.7 15618.5± 0.8 15616.5± 0.6 15615.6± 0.7	15650.5± 1.6 15652.9± 0.9 15652.3± 0.9 15654.5± 0.8 15650.5± 1.4 15652.7± 0.6 15651.3± 0.8 15650.1± 0.5	15670.7± 0.5 15673.3± 0.2 15672.0± 0.3 15672.7± 0.3 15671.7± 0.3 15672.2± 0.6 15672.0± 0.3 15670.7± 0.3	+14.29 -40.27 -7.93 -21.23 +1.50 -22.64 +2.66 +15.70
	J04370204+4205520	161.18	-3.426	29± 29	15± 22	39±22	15616.2± 0.6	15651.3± 0.8	15672.2± 0.4	-15.61

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J04375966+5753122 J04415341+3905530 J04420018+5845056	149.52 164.05 149.21	+7.241 -4.726 +8.206	. . . ± . . . 18± 14 24± 26	18± 12 10± 12 23± 15	50±28 53±22 48±13	. . . ± . . . 15615.8± 0.4 15615.6± 0.8	15651.2± 0.3 15650.2± 0.7 15651.7± 0.7	15670.8± 0.4 15671.7± 0.4 15670.9± 0.2	+6.65 +15.12 +6.84
	J04465646+3945400 J04470590+3935397 J04483353+4019230 J04490118+4011539 J04491941+3915544 J04492766+3932129 J04493447+3932429 J04495502+4001234 J04505777+4011243 J04592513+4855187	164.18 164.33 163.95 164.10 164.86 164.67 164.67 164.35 164.35 158.49	-3.554 -3.639 -2.955 -2.968 -3.521 -3.327 -3.305 -2.948 -2.688 +3.965	15± 15 25± 18 19± 14 15± 23 16± 20 25± 21 9± 9 17± 18 42± 28 20± 27	. . . ± . . . 37± 28 48± 37 22± 20 . . . ± . . . 17± 21 . . . ± . . . . . . ± . . . . . . ± . . . 23± 31	35±15 36±11 23±17 41±17 23±11 31±13 45±14 48±19 29±11 70±28	15615.4± 0.6 15615.8± 0.8 15615.1± 0.5 15618.6± 1.6 15615.8± 1.2 15615.1± 0.7 15615.8± 0.6 15614.2± 1.1 15617.6± 0.7 15615.1± 0.7	. . . ± . . . 15651.1± 2.1 15652.0± 1.2 15650.7± 1.2 . . . ± . . . 15649.8± 2.3 . . . ± . . . . . . ± . . . . . . ± . . . 15649.7± 0.9	15671.8± 0.3 15671.6± 0.3 15671.2± 0.4 15672.6± 0.4 15671.4± 0.4 15671.4± 0.4 15672.2± 0.2 15671.0± 0.3 15670.6± 0.3 15671.2± 0.3	+4.91 +9.32 +13.24 +0.36 +14.07 +15.57 -6.10 +18.94 +23.70 +17.75
	J05000982+2235338 J05010801+2326203 J05021095+2330182 J05032736+4004359 J05035397+4040598 J05041580+3550511 J05041915+4136083	179.53 178.97 179.07 165.92 165.49 169.38 164.81	-12.022 -11.340 -11.107 -0.891 -0.455 -3.334 +0.166	12± 23 41± 27 21± 23 5± 9 8± 14 . . . ± . . . 13± 20	42± 28 11± 12 36± 29 21± 16 14± 13 31± 31 23± 16	22±13 25±13 43±24 54±17 37±16 85±31 19±10	15615.6± 1.3 15615.7± 0.7 15617.7± 0.7 15616.3± 0.9 15615.4± 1.0 . . . ± . . . 15616.9± 1.5	15650.8± 0.5 15652.1± 0.7 15653.3± 1.7 15652.1± 0.7 15650.9± 0.7 15651.2± 1.6 15650.9± 0.7	15670.8± 0.2 15671.6± 0.4 15672.0± 0.4 15672.9± 0.4 15672.3± 0.5 15671.5± 0.2 15671.8± 0.5	+30.92 +15.09 -2.18 -13.69 +6.77 +16.98 +7.66
	J05042205+2138367 J05043026+3539583 J05045266+4225516	180.90 169.56 164.22	-11.793 -3.405 +0.751	36± 27 16± 22 12± 17	76± 36 40± 23 17± 14	60±24 43±20 17±15	15618.8± 0.8 15615.9± 0.7 15614.0± 0.8	15649.9± 0.5 15650.5± 0.5 15650.3± 0.5	15670.4± 0.3 15671.3± 0.2 15670.5± 0.6	+35.73 +15.77 +30.98
	J05051685+4006442 J05091936+4206041 J05102070+3333407 J05125815+2449470 J05131788+2450164 J05143594+2453505 J05154849+3737332 J05154990+2511030 J05155196+2450235 J05160193+2544064 J05161625+2355236 J05163119+2959412	166.11 164.97 171.97 179.43 179.47 179.59 169.32 179.51 179.80 179.08 180.62 175.64	-0.592 +1.211 -3.700 -8.336 -8.270 -7.995 -0.417 -7.601 -7.792 -7.250 -8.238 -4.716	31± 27 48± 39 63± 43 18± 12 22± 17 8± 7 17± 17 29± 17 15± 14 21± 16 15± 13 30± 23	10± 12 11± 15 82± 60 12± 9 78± 45 . . . ± . . . . . . ± . . . . . . ± . . . 57± 36 15± 20 27± 16 31± 31	37±14 57±29 112±26 43±11 58±15 45±14 34±19 51±12 59±11 50±23 87±26 37±28	15617.6± 0.9 15616.0± 0.6 15616.2± 0.4 15615.7± 0.6 15616.7± 0.7 15616.9± 0.4 15614.8± 0.7 15614.8± 0.6 15615.1± 0.9 15616.7± 0.6 15615.5± 0.5 15614.2± 0.3	15650.6± 0.8 15650.5± 0.5 15650.7± 1.2 15650.9± 1.3 15652.9± 1.2 . . . ± . . . . . . ± . . . . . . ± . . . 15652.3± 1.2 15650.7± 3.0 15650.2± 0.6 15647.0± 0.4	15671.0± 0.4 15670.7± 0.3 15671.8± 0.1 15671.7± 0.2 15673.1± 0.2 15672.6± 0.2 15672.3± 0.2 15669.9± 0.2 15671.3± 0.2 15672.9± 0.4 15672.1± 0.2 15669.9± 0.3	+14.54 -0.74 +1.62 +12.58 -7.84 -4.03 -6.32 +48.57 +20.50 +2.21 +10.50 +40.01

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J05172665+2536318 J05183271+3934291 J05192542+3019435 J05195542+3922562	179.37 168.04 175.72 168.35	-7.060 +1.146 -4.011 +1.254	7± 14 39± 33 48± 31 19± 22	15± 13 31± 33 . . . ± . . . 47± 27	35±14 47±24 54±30 21±12	15616.7± 0.8 15616.1± 0.7 15614.5± 0.6 15614.3± 1.0	15651.1± 0.4 15650.0± 1.9 . . . ± . . . 15648.5± 0.9	15672.6± 0.3 15672.5± 0.4 15672.7± 0.4 15671.9± 0.4	-2.16 -2.24 +3.93 +5.21
	J05201627+4427482	164.21	+4.206	13± 12	21± 18	22±12	15616.3± 0.5	15651.3± 1.0	15671.6± 0.3	+2.81
	J05201669+4400087 J05205203+3102072 J05212271+3600479 J05223507+3618015 J05225322+2905377 J05242835+3011309 J05251863+3010006 J05275100+2731138 J05284397+2716152 J05303582+2744298 J05324662+2644507 J05325168+3305416 J05380741+2418520 J05422067+2943348 J05431996+2757575 J05440485+2930048	164.59 175.32 171.28 171.19 177.17 176.46 176.58 179.09 179.41 179.24 180.34 175.02 183.05 178.95 180.56 179.34	+3.945 -3.353 -0.433 -0.069 -4.091 -3.189 -3.053 -4.062 -4.036 -3.432 -3.568 -0.096 -3.859 -0.194 -0.936 +0.009	32± 26 31± 30 14± 17 13± 12 74± 38 17± 16 24± 33 20± 16 24± 22 21± 17 17± 25 21± 17 48± 31 21± 16 22± 14 14± 14	49± 26 59± 52 . . . ± . . . 59± 39 53± 41 27± 27 16± 15 37± 29 19± 16 22± 19 81± 47 . . . ± . . . 85± 47 76± 38 56± 33 68± 35	32±18 49±16 26±17 29±20 48±25 56±22 70±22 36±14 47±16 40±14 45±15 83±32 43±23 46±18 42±20 24±12	15615.9± 0.9 15615.9± 0.6 15615.7± 0.7 15615.8± 0.5 15615.8± 0.4 15615.5± 0.4 15617.0± 1.2 15614.9± 0.6 15615.9± 0.9 15614.9± 0.5 15615.1± 1.0 15615.9± 0.2 15619.5± 0.6 15616.1± 0.7 15615.8± 0.6 15615.4± 0.7	15651.8± 0.9 15653.2± 0.7 . . . ± . . . 15649.8± 1.3 15652.1± 1.1 15651.6± 2.1 15650.0± 0.5 15650.6± 1.5 15650.7± 0.9 15649.8± 0.6 15649.8± 0.9 . . . ± . . . 15650.6± 1.0 15649.7± 0.9 15650.3± 1.8 15651.0± 1.0	15672.4± 0.4 15671.1± 0.2 15671.4± 0.4 15673.1± 0.6 15672.2± 0.3 15672.2± 0.3 15671.6± 0.3 15670.8± 0.2 15671.6± 0.3 15671.0± 0.2 15671.0± 0.3 15671.2± 0.2 15672.8± 0.4 15671.5± 0.4 15671.6± 0.5 15671.4± 0.5	-18.33 +16.14 +7.49 +8.77 +3.59 +4.56 +20.96 +20.44 +14.38 +23.97 +34.95 +3.54 +21.14 +18.68 +17.87 +18.71
	J05441785+3001084	178.93	+0.320	17± 13	46± 19	20±8	15617.2± 0.8	15652.2± 0.7	15672.2± 0.3	+2.19
	J05442116+2743534 J05453652+3010038	180.88 178.95	-0.867 +0.640	12± 14 29± 17	. . . ± . . . 41± 17	27±14 30±11	15616.6± 1.0 15615.9± 0.6	. . . ± . . . 15651.6± 0.5	15673.4± 0.7 15671.8± 0.3	+4.72 +7.85
	J05464534+2927536	179.68	+0.487	15± 15	75± 56	32±15	15616.0± 0.8	15651.6± 1.6	15672.0± 0.4	+15.22
	J05464904+1212013 J05465678+2948516	194.54 179.40	-8.376 +0.703	23± 29 18± 13	70± 49 40± 25	55±24 25±16	15618.6± 1.3 15615.4± 0.6	15650.4± 1.1 15651.0± 1.3	15670.8± 0.3 15671.4± 0.5	+39.15 +20.16
	J05470989+2925504	179.75	+0.545	11± 12	33± 17	19±10	15615.5± 0.8	15651.5± 0.7	15671.5± 0.5	+16.40
	J05484133+2819285	180.87	+0.259	35± 23	78± 36	56±18	15616.0± 0.6	15650.3± 0.6	15671.8± 0.2	+12.99
	J05490737+3002444 J05492199+2035035 J05502984+1323001 J05561354+3142415	179.45 187.59 193.97 178.78	+1.227 -3.588 -7.006 +3.385	18± 16 32± 30 10± 15 12± 12	. . . ± . . . 29± 20 35± 21 36± 38	51±16 37±20 21±17 34±19	15615.5± 0.7 15616.8± 0.9 15616.0± 1.0 15616.2± 0.6	. . . ± . . . 15650.6± 0.9 15649.4± 0.7 15649.1± 1.9	15671.6± 0.3 15671.7± 0.4 15674.1± 0.9 15671.6± 0.3	+19.86 +20.84 +25.52 +11.41
	J06001327+1408224 J06013301+3126285	194.49 179.58	-4.581 +4.236	12± 12 22± 25	. . . ± . . . . . . ± . . .	36±12 29±14	15616.2± 0.6 15615.6± 1.3	. . . ± . . . . . . ± . . .	15671.7± 0.2 15672.6± 0.4	+11.59 -2.37

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J06021392+1452593 J06172091+0747140 J06174008-0056459 J06180772-0022145 J06190302-0035144 J06201567+0424355 J06203139+1615067	194.08 202.10 209.91 209.45 209.75 205.43 195.00	-3.792 -3.950 -7.942 -7.576 -7.471 -4.892 +0.728	23± 24 24± 27 20± 20 20± 15 23± 24 42± 29 17± 18	35± 19 . . . ± . . . . . . ± . . . 23± 21 . . . ± . . . . . . ± . . . 26± 29	36±16 46±30 66±16 54±11 66±16 25±11 30±14	15613.1± 1.2 15615.0± 0.4 15615.4± 0.4 15614.9± 0.6 15616.1± 0.9 15617.0± 0.6 15617.0± 1.1	15649.6± 0.6 . . . ± . . . . . . ± . . . 15650.7± 1.3 . . . ± . . . . . . ± . . . 15649.8± 2.6	15671.1± 0.5 15670.3± 0.3 15671.8± 0.1 15670.7± 0.2 15672.6± 0.2 15671.0± 0.2 15672.4± 0.5	+30.52 +64.24 +25.73 +42.49 +9.16 +30.29 +4.76
	J06205811+1655496 J06210046+0825395 J06210794+2209569	194.45 201.96 189.84	+1.142 -2.850 +3.633	29± 19 16± 22 28± 38	. . . ± . . . . . . ± . . . 46± 56	37±18 34±21 54±19	15615.1± 0.7 15616.0± 0.8 15617.2± 1.0	. . . ± . . . . . . ± . . . 15648.2± 1.9	15671.3± 0.4 15670.8± 0.3 15671.8± 0.3	+27.91 +29.98 +15.33
	J06214549+3400594	179.30	+9.197	21± 23	17± 14	35±19	15615.4± 0.9	15650.6± 0.7	15671.3± 0.5	+11.94
	J06315253+0501591 J06351414+0114582 J06372199+0558179 J06372390+0604442 J06420526+0012241 J06421206-0001180 J06443656+0035213 J06455605-0126035 J06460026-0451501 J06575656+0500002	206.22 209.97 206.02 205.92 211.68 211.90 211.63 213.58 216.65 209.22	-2.039 -3.033 -0.394 -0.337 -1.986 -2.065 -1.250 -1.878 -3.422 +3.721	25± 17 25± 22 10± 11 14± 20 7± 20 54± 35 42± 27 20± 17 16± 32 8± 11	30± 18 . . . ± . . . 64± 35 33± 32 26± 35 . . . ± . . . . . . ± . . . 23± 38 34± 28 5± 8	47±12 18±14 28±16 29±17 19±14 29±21 36±17 17±8 49±31 10±11	15613.9± 0.6 15615.6± 0.8 15616.4± 0.8 15615.2± 1.4 15614.9± 1.5 15614.3± 0.5 15615.9± 0.6 15614.7± 0.4 15618.2± 1.2 15616.6± 0.8	15649.4± 0.6 . . . ± . . . 15648.3± 1.8 15648.1± 2.2 15648.1± 2.0 . . . ± . . . . . . ± . . . 15651.5± 4.6 15651.7± 0.7 15650.3± 0.6	15670.0± 0.3 15671.2± 0.4 15672.2± 0.7 15672.8± 0.6 15670.5± 0.3 15670.8± 0.4 15671.0± 0.3 15671.2± 0.1 15672.8± 0.3 15671.2± 0.7	+56.39 +31.52 +18.59 +13.80 +48.67 +41.86 +36.51 +40.53 +10.99 +31.23
	J07290723-2003523 J16295213-1222516	234.99 3.53	-1.123 +23.935	30± 21 28± 22	. . . ± . . . 18± 20	28±25 24±20	15615.6± 0.2 15615.8± 0.3	. . . ± . . . 15651.5± 0.9	15671.9± 0.3 15671.2± 0.4	+36.57 +1.36
	J16302979-1217509	3.70	+23.864	12± 14	14± 16	17±11	15613.9± 0.9	15651.5± 2.0	15671.0± 0.4	+0.19
	J16313011-1248244	3.42	+23.360	7± 14	27± 20	22±14	15615.3± 1.5	15650.1± 1.3	15670.5± 0.5	+5.40
	J17181515-2906309	356.58	+4.917	9± 13	67± 32	41±19	15616.7± 0.6	15651.1± 0.8	15670.1± 0.2	+24.70
	J17210564-2757272	357.89	+5.057	27± 54	22± 29	54±35	15615.8± 1.1	15651.4± 0.7	15671.2± 0.3	+19.01
	J17330783-2946169 J17341553-2913063 J17344915-2456492	357.84 358.44 2.11	+1.859 +1.953 +4.155	. . . ± . . . 66± 38 32± 29	. . . ± . . . 69± 41 49± 26	29±20 34±21 61±28	. . . ± . . . 15617.1± 0.3 15614.7± 0.4	. . . ± . . . 15652.3± 0.4 15650.1± 0.2	15672.0± 0.2 15671.7± 0.2 15669.0± 0.2	-7.60 -14.72 +34.21
	J17345020-2917115 J17345235-2722089 J17353172-2718506 J17363478-2749022 J17395551-2908460	358.45 0.07 0.20 359.90 359.17	+1.810 +2.839 +2.746 +2.279 +0.948	37± 34 29± 19 72± 57 55± 60 . . . ± . . .	88± 56 27± 19 . . . ± . . . 39± 29 . . . ± . . .	71±25 65±21 114±51 79±45 91±35	15616.0± 0.4 15617.4± 0.3 15617.0± 0.4 15613.4± 0.7 . . . ± . . .	15652.2± 1.1 15653.1± 0.4 . . . ± . . . 15650.9± 0.2 . . . ± . . .	15672.0± 0.1 15673.4± 0.2 15672.2± 0.2 15672.5± 0.2 15671.6± 0.2	-0.57 -22.16 -12.19 -6.43 -0.30

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J17425688-2826194	0.12	+0.760	33± 21	43± 25	49±14	15614.3± 0.4	15650.1± 0.4	15670.6± 0.1	+16.41
	J17434172-2437561 J17435446-2814306	3.45 0.40	+2.614 +0.683	. . . ± . . . 37± 21	55± 39 53± 27	42±32 56±20	. . . ± . . . 15614.8± 0.4	15649.5± 0.2 15650.2± 0.8	15670.5± 0.2 15669.9± 0.3	+14.40 +32.63
	J17443145-2533198 J17452350-2802425	2.76 0.73	+1.972 +0.507	. . . ± . . . 9± 22	40± 27 27± 26	80±37 38±19	. . . ± . . . 15616.0± 1.5	15649.8± 0.3 15651.1± 1.0	15670.1± 0.2 15671.5± 0.4	+30.74 +4.11
	J17453251-2805260	0.71	+0.455	59± 35	100± 70	69±33	15615.6± 0.3	15651.5± 1.2	15671.3± 0.2	+9.47
	J17483083-2237243 J17483083-2237243 J17494346-2303468	5.74 5.74 5.51	+2.712 +2.712 +2.248	. . . ± . . . . . . ± . . . 39± 24	39± 29 45± 46 32± 36	50±28 61±37 70±29	. . . ± . . . . . . ± . . . 15614.1± 0.2	15650.8± 0.5 15649.4± 0.8 15651.4± 0.6	15670.5± 0.3 15669.9± 0.3 15670.2± 0.1	+7.75 +27.12 +17.01
	J17494563-2339013 J17510378-2930227 J17515961-3036450 J17531540-2246568 J17554830-2125123 J17575299-2330566	5.01 0.12 359.28 6.16 7.64 6.06	+1.939 -1.311 -2.049 +1.691 +1.869 +0.403	. . . ± . . . 30± 23 22± 26 17± 36 24± 31 50± 51	67± 48 . . . ± . . . 128± 74 44± 40 45± 20 203± 83	53±40 60±36 56±20 23±24 41±24 51±31	. . . ± . . . 15615.0± 0.2 15618.0± 0.9 15617.1± 0.8 15616.2± 0.6 15614.9± 0.6	15651.4± 0.7 . . . ± . . . 15651.0± 1.1 15651.1± 0.4 15651.9± 0.2 15651.7± 0.6	15670.7± 0.3 15672.2± 0.3 15672.1± 0.2 15671.7± 0.4 15671.8± 0.3 15670.5± 0.2	+9.77 -5.20 -14.85 -4.34 -4.87 +27.12
	J17585262-2321007	6.32	+0.288	30± 27	39± 32	37±25	15617.6± 0.3	15652.3± 0.7	15671.6± 0.2	+4.94
	J17595744-2329429 J18000670-2320085	6.32 6.47	+0.001 +0.049	64± 48 20± 23	. . . ± . . . 25± 29	95±40 37±27	15615.7± 0.3 15613.5± 0.4	. . . ± . . . 15651.0± 0.9	15671.9± 0.2 15671.0± 0.3	+7.38 +20.75
	J18001626-2324407 J18002789-2949276 J18021640-1721376 J18124109-2538325 J18140101+0011580	6.43 0.88 11.92 5.85 28.82	-0.020 -3.237 +2.566 -3.572 +8.410	55± 33 2± 5 . . . ± . . . . . . ± . . . 5± 14	. . . ± . . . 38± 25 78± 61 47± 43 28± 19	53±22 40±16 47±31 51±29 21±15	15615.3± 0.4 15619.5± 0.5 . . . ± . . . . . . ± . . . 15616.5± 2.0	. . . ± . . . 15648.4± 0.8 15652.5± 0.7 15651.4± 0.6 15649.5± 0.8	15670.2± 0.2 15670.8± 0.3 15671.4± 0.2 15671.4± 0.2 15669.5± 0.8	+26.75 +15.62 +1.83 -2.28 +13.92
	J18140179-1439398 J18161215-1439207 J18171609-1527061 J18175339-1427506 J18190297-1206360	15.65 15.91 15.33 16.27 18.48	+1.404 +0.945 +0.340 +0.677 +1.542	34± 45 37± 47 34± 55 14± 12 71± 45	. . . ± . . . 44± 26 . . . ± . . . 11± 21 58± 38	24±15 60±24 75±24 24±20 70±21	15615.8± 0.9 15618.7± 0.8 15615.0± 0.4 15615.9± 0.2 15612.6± 0.4	. . . ± . . . 15651.6± 0.3 . . . ± . . . 15651.2± 0.7 15648.5± 0.6	15671.4± 0.2 15671.6± 0.2 15671.5± 0.2 15671.5± 0.3 15670.8± 0.2	-11.09 -3.83 -1.01 -6.20 +23.24
	J18214300-1740026 J18215744+0140512	13.88 31.06	-1.646 +7.325	23± 27 21± 18	53± 44 16± 20	50±29 20±24	15615.2± 0.5 15616.0± 0.6	15651.0± 0.7 15649.1± 1.2	15670.7± 0.2 15670.3± 0.9	+12.03 +19.00
	J18222774-0836442	21.96	+2.446	44± 38	49± 33	51±31	15615.1± 0.4	15651.6± 0.3	15671.0± 0.2	+8.28
	J18223170-1718106 J18231720-0523192	14.29 24.91	-1.646 +3.769	16± 25 4± 26	23± 23 53± 30	20±18 29±18	15614.5± 0.8 15614.4± 3.6	15648.9± 0.5 15649.5± 0.4	15670.5± 0.4 15670.2± 0.4	+18.08 +16.04
	J18232722-1257110	18.24	+0.197	33± 22	45± 24	46±19	15615.5± 0.5	15650.5± 0.6	15669.7± 0.2	+25.91
	J18252154-1120175	19.89	+0.541	42± 29	120± 67	138±53	15613.9± 0.2	15651.1± 0.5	15670.2± 0.1	+33.64

Table 65 :

 65 continued.

	2MASS ID	GLON (deg)	GLAT (deg)	EW 15617 (m Å)	EW 15653 (m Å)	EW 15673 (m Å)	λ c 15617 ( Å)	λ c 15653 ( Å)	λ c 15673 ( Å)	Vrad (km.s -1 )
	J18281533-0911334	22.12	+0.910	36± 56	51± 40	27±13	15617.4± 0.8	15651.3± 0.6	15670.1± 0.1	+10.39
	J18362077-0642161	25.25	+0.282	36± 31	39± 33	37±20	15613.9± 0.4	15649.5± 0.6	15670.2± 0.3	+23.49
	J18373565-0715423 J18383229-1111196 J18401462-0242174	24.90 21.51 29.25	-0.249 -2.256 +1.254	37± 32 26± 31 17± 21	34± 27 67± 39 34± 25	42±27 34±20 28±12	15614.9± 0.6 15616.4± 0.5 15614.5± 1.1	15649.5± 0.6 15650.0± 0.4 15651.3± 2.5	15670.8± 0.4 15669.4± 0.2 15670.8± 0.3	+21.77 +33.19 +7.07
	J18403188-0219215	29.62	+1.365	29± 18	77± 63	39±20	15615.0± 0.6	15652.0± 1.9	15670.1± 0.5	+24.60
	J18411671-0234002	29.49	+1.088	25± 13	100± 39	46±19	15615.2± 0.4	15651.3± 0.8	15671.2± 0.3	+14.50
	J18432830-0220047	29.95	+0.706	38± 19	52± 28	61±15	15614.4± 0.5	15649.3± 2.1	15669.8± 0.2	+32.60
	J18433158+0502488	36.53	+4.055	10± 11	31± 27	30±17	15615.2± 0.3	15651.1± 0.4	15671.6± 0.2	+0.57
	J18434869+0631442 J18434968-0157426	37.89 30.32	+4.661 +0.798	25± 21 27± 18	16± 23 31± 20	28±23 23±11	15617.4± 0.6 15614.2± 0.6	15652.5± 1.0 15649.2± 0.8	15672.4± 0.6 15669.9± 0.4	-15.14 +21.43
	J18441756-0215328 J18444099-0234170	30.11 29.87	+0.559 +0.329	26± 14 38± 20	. . . ± . . . 58± 28	40±13 44±15	15614.9± 0.6 15614.9± 0.5	. . . ± . . . 15650.6± 0.9	15670.9± 0.3 15670.7± 0.3	+3.05 +12.62
	J18445041-0332478 J18451660-0156532	29.02 30.50	-0.151 +0.482	7± 44 22± 17	21± 22 26± 21	25±17 42±14	15614.0± 1.5 15614.7± 0.8	15650.6± 0.4 15650.2± 3.0	15669.9± 0.8 15670.3± 0.3	+14.20 +19.92
	J18464780-0201134 J18471949+1345555 J18474175+1326322 J18490823-0808488 J18534172+1412156	30.60 44.78 44.53 25.41 45.87	+0.111 +7.124 +6.900 -3.197 +5.935	28± 16 20± 17 12± 18 10± 13 40± 35	47± 21 25± 24 49± 31 44± 36 27± 24	40±12 33±24 13±12 28±18 26±12	15617.0± 0.8 15617.4± 0.4 15615.9± 0.7 15616.0± 0.4 15614.3± 0.7	15653.3± 1.0 15651.3± 0.8 15651.1± 0.6 15649.7± 0.7 15650.9± 0.6	15672.5± 0.5 15672.7± 0.6 15671.6± 0.5 15671.0± 0.3 15670.6± 0.2	-25.21 -14.71 -6.94 -4.36 +4.70
	J18544633+1428396 J18545937-0348492 J18560093-0334332 J18574090+0458568	46.24 29.94 30.27 38.08	+5.823 -2.527 -2.647 +0.885	10± 11 27± 16 22± 18 34± 41	20± 22 35± 21 36± 24 87± 59	63±28 35±17 62±25 28±17	15614.6± 0.2 15615.2± 0.5 15616.0± 0.6 15615.3± 0.8	15651.8± 0.5 15649.6± 1.2 15650.9± 0.9 15652.2± 0.7	15672.1± 0.3 15670.5± 0.3 15671.7± 0.3 15671.0± 0.3	-3.39 +16.00 +0.14 +3.43
	J18575755-0305423 J19011320+0510259	30.92 38.65	-2.860 +0.189	40± 23 32± 21	29± 25 28± 22	31±13 25±21	15617.1± 0.7 15614.7± 0.4	15651.6± 2.9 15650.5± 0.5	15672.2± 0.3 15671.0± 0.5	-16.94 +7.81
	J19013180-0329313 J19015818-0351352 J19020621+0301121 J19024013-0323036 J19030094-0320315 J19030644-0330494 J19050783-0410492 J19082768+0231104 J19100158+1053412	30.97 30.69 36.84 31.19 31.27 31.13 30.76 37.12 44.73	-3.835 -4.099 -0.992 -4.039 -4.097 -4.195 -4.947 -2.633 +0.881	15± 17 42± 21 33± 36 26± 20 31± 16 16± 21 12± 16 31± 28 39± 19	23± 22 37± 22 . . . ± . . . 19± 18 65± 41 14± 15 46± 26 22± 24 57± 29	28±16 41±13 40±21 14±9 34±15 18±13 21±14 24±16 21±11	15616.6± 0.8 15614.3± 0.4 15616.3± 0.9 15615.8± 0.4 15615.3± 0.6 15615.8± 1.3 15614.5± 0.9 15615.4± 0.7 15615.3± 0.4	15651.5± 1.1 15650.3± 0.9 . . . ± . . . 15650.9± 0.7 15651.6± 1.2 15650.2± 1.3 15651.2± 1.1 15649.2± 0.8 15651.8± 0.5	15671.4± 0.5 15670.3± 0.3 15672.2± 0.4 15671.6± 0.4 15670.5± 0.3 15670.8± 0.5 15670.3± 0.6 15670.4± 0.3 15671.1± 0.2	-13.92 +17.59 -11.75 -4.83 +21.08 +0.60 +17.94 +28.29 -6.19

Table 65 :

 65 continued.

	2MASS ID	GLON	GLAT	EW 15617	EW 15653	EW 15673	λ c 15617

Table 65 :

 65 continued.

	1 )

Table 1 .

 1 UVES instrument setups used.Notes. The two spectrograph arms are used to collect data for a pair of wavelength regions simultaneously. A second setup allows the gaps to be covered with another pair of settings. For each setting we give the slit width, nominal resolving power, and nominal wavelengths covered. Red-L and Red-U refer to the spectra recorded with the Red Lower EEV CCD and the Red Upper MIT CCD (cf. Sect. 5).

	1 http://www.eso.org/observing/dfo/quality/UVES/
	reports/HEALTH/trend_report_ECH_RESOLUTION_DHC_HC.html
	A76, page 3 of 29

Table 2 .

 2 Wavelength calibration uncertainties for the thorium-argon frames corresponding each of the observed UVES settings for HD 23180.

	Arm	Uncertainty (Å) Uncertainty (m s -1 )
	346-nm 437-nm 564-nm L 564-nm U 860-nm L 860-nm U	6.4 × 10 -4 7.8 × 10 -4 4.5 × 10 -4 7.0 × 10 -4 8.3 × 10 -4 1.1 × 10 -3	52 54 26 34 33 36
	caused by emission features in the deuterium lamp redwards of 321 nm.

Table 3 .

 3 Median S/N per "resolution-element" (0.04 Å spectral bin) for each UVES setting/arm configuration for all available EDIBLES spectra.

	Setting Arm	λ-range (nm)	Median S /N Sample
				(0.04 Å bin) size
	346 437	blue blue	339.3-339.4 398.8-398.	780 a	204

Table A . 1 .

 A1 Targets observed for EDIBLES.

	A76, page 18 of 29	Identifier Table A.1. continued. Identifier	RA J2000 RA J2000	Dec J2000 Dec J2000	Spectral type Spectral type	Ref. E(B -V) R V mag Ref. E(B -V) R V mag	A V mag A V mag	Ref. log N(H i) log N(H 2 ) f H 2 cm -2 cm -2 cm -2 cm -2 Ref. log N(H i) log N(H 2 ) f H 2
	A76, page 17 of 29	HD 03:42:22.7 +33:57:54.1 B0.5 V HD 03:42:19.0 +19:42:00.9 B8 Ve HD 03:44:19.1 +32:17:17.7 B1 III HD 03:54:07.9 +31:53:01.1 B1 Ib HD 04:23:59.8 +24:18:03.6 B3 V HD 05:19:35.3 -01:24:42.8 B1.5 Vn HD 05:32:00.4 -00:17:56.7 O9.5 II Nwk HD 05:33:31.5 -01:09:21.9 B1 V HD 05:34:49.2 +09:29:22.5 B0 III HD 05:35:08.3 +09:56:03.0 O8 III ((f)) HD 05:35:16.5 -05:23:23.2 O7 Vp HD 05:35:22.9 -05:24:57.8 O9.5 IVp HD 05:35:31.4 -05:16:02.6 B0.5 V HD 05:39:18.3 +29:12:54.8 B2 IV-V HD 05:41:38.4 -02:15:32.5 B1.5 V HD 05:47:45.4 -09:40:10.6 B0.5 Ia HD 05:57:59.7 +25:57:14.1 B1 Ib HD 06:03:55.2 +20:08:18.4 B2 Ia HD 06:16:58.7 +23:44:27.3 B3 Iab HD 07:06:36.0 -12:23:38.0 O7.5 Vz HD 07:08:23.2 -11:51:08.6 B2 IIIn HD 07:09:20.3 -10:20:47.8 O7 Vz var? (SB2) HD 07:14:28.3 -10:18:58.5 O9.7 III HD 07:18:42.5 -24:57:15.8 O9 II (SB2+SBE) HD 07:58:50.6 -60:49:28.1 B2 Vne HD 08:03:35.1 -40:00:11.3 O4 I(n)fp HD 08:39:09.5 -40:25:09.3 O8.5 IV (SB1+SBE) S14 M55 0.23 S99 0.05 G77 0.28 L68 0.29 O59 0.36 L68 0.13 S11 0.16 L68 0.08 L75 0.14 S11 0.13 S11 0.31 S11 0.21 S71 0.52 L68 0.37 S52 0.34 W71 0.07 L68 0.11 W71 0.41 L68 0.54 S11 0.20 M55 0.27 S14 0.32 S11 0.13 S14 0.12 A69 0.15 S14 0.08 0.68 HD 08:47:28.0 -46:27:04.0 B2 Ib/II H78 0.16 HD 08:50:53.2 -43:45:05.4 BC2 Iab W76 0.87 HD 09:11:04.4 -44:52:04.4 B5 Ia H69 0.28 HD 09:18:42.4 -51:33:38.3 B6 Ia H69 0.57 HD 09:22:06.8 -55:00:38.4 B2 IV L75 0.10 HD 10:34:46.6 -58:09:22.0 O7 V((f))z (SB1) S14 0.25 HD 10:42:57.4 -64:23:40.0 B0.2 Vp W76 0.08 HD 10:44:33.7 -59:44:15.5 O3.5 V((f)) + O8 V S14 0.34 HD 10:44:36.2 -60:05:29.0 O7 V((f))z S14 0.34 HD 93843 10:48:37.8 -60:13:25.5 O5 III(fc) SB1? S14 0.27 HD 94493 10:53:15.1 -60:48:53.2 B0.5 Ib: H73 0.23 HD 99953 11:29:15.1 -63:33:14.2 B2 Ia M55 0.45 HD 11:56:58.0 -63:14:56.7 B0.5 Iab G77 0.24 HD 12:03:23.9 -62:41:45.8 B0.5 III M55 0.23 HD 12:35:16.5 -72:43:00.8 B0.5 Ib H73 0.26 HD 12:53:37.6 -60:21:25.4 B1.5 Ib E05 0.34 HD 12:56:33.7 -64:21:39.2 B0.5 Ia M55 0.85 HD 13:08:07.2 -65:18:21.7 O9 III S14 0.20 HD 13:30:23.5 -78:51:20.5 O8.5 II-III((f)) S14 0.20 HD 14:06:25.2 -59:42:57.3 B0 Ia W76 0.34 HD 14:15:01.6 -61:42:24.4 O6 IV(n)((f)) S14 0.49 HD 15:06:56.0 -52:01:47.2 B2 Vp G77 0.13 HD 15:18:49.1 -60:18:51.5 O8 IV((f)) S14 0.20 HD 16:00:20.0 -22:37:18.1 B0.3 IV M73 0.19 HD 16:06:48.4 -20:40:09.1 B1 V W71 0.21 HD 16:11:59.7 -19:27:38.5 B2 V (SB2) L75 0.27 HD 16:20:38.2 -24:10:09.8 A5 II G01 0.78 HD 16:21:11.3 -25:35:34.1 B1 III W71 0.37 HD 16 24 43.7 -34 53 37.5 B3: Vn (SB2) G77 0.29 HD 16:25:24.3 -23:27:36.8 B3 V: F57 0.44 HD 16:25:24.3 -24:21:07.1 B2 V H56 1.03 HD 16:25:35.1 -23:26:48.7 B2 IV H69 0.43 HD 16:25:35.0 -23:26:46.0 B2 V H69 0.43 HD 16:30:12.5 -25:06:54.8 B2 V M73 0.07 HD 16:33:52.4 -48:06:40.5 O6 f?p S14 0.65 HD 16:34:05.0 -44:02:43.1 O9.7 Iab S14 0.31 HD 16:36:22.6 -42:51:31.9 O8.5 Iab(f)p (SB2) S14 0.67 HD 16:37:09.5 -10:34:01.5 O9.2 IVnn S14 0.32 HD 16:41:20.4 -48:45:46.8 O3.5-4 III(f*) + O6 IV S14 0.43 HD 16:51:33.7 -41:13:49.9 O8 Iaf (SB2) S14 0.34 HD 16:54:10.1 -41:49:30.1 O7 Iabf + O7 Ib(f) S14 0.47 HD 16:54:58.5 -41:09:03.1 O8: Iafpe S14 0.44 HD 16:55:03.3 -42:05:27.0 OC9.2 Ia (SB1) S14 0.66 HD 17:03:56.8 -37:50:38.9 O6 Iafcp (SB1E) S14 0.59 HD 17:05:18.9 -47:04:08.5 B2 Iab H78 0.80	-2.30 0.12 W03 --21.04 3.11 0.93 V04 20.88 2.63 0.89 W03 20.8 2.59 0.91 V04 20.35 2.61 0.29 W03 21.16 --20.19 2.45 0.15 W03 20.7 2.64 0.21 W03 20.81 2.46 0.22 W03 20.78 5.73 1.78 V04 21.54 5.67 1.13 V04 -4.29 2.40 V04 -3.55 1.49 V04 21.17 3.74 1.31 V04 21.12 --20.52 3.05 0.37 W03 20.9 3.12 1.25 V04 20.15 3.29 1.91 W03 21.27 3.25 0.62 W03 21.13 2.73 0.79 V04 -3.12 0.81 W03 21.38 2.47 0.15 W03 20.9 3.57 0.36 W03 20.7 2.25 0.41 W03 -5.01 0.25 W03 19.96 3.56 2.46 V04 21.11 3.53 1.02 V04 21.07 3.44 3.10 W03 -3.21 1.28 V04 21.18 3.35 2.01 W03 ---19.95 3.35 0.80 V04 21.12 2.43 0.10 W03 20.26 3.25 1.24 V04 21.38 4.76 1.71 V04 21.4 3.89 0.66 V04 21.33 3.57 0.82 V04 21.08 3.69 1.77 V04 -3.29 0.69 V04 21.16 2.81 0.65 V04 21.1 3.26 0.68 W03 21.11 2.45 1.25 V04 -3.09 3.09 V04 -3.63 0.76 W03 21.08 2.42 0.51 V04 20.96 3.15 1.13 V04 21.26 2.97 1.49 W03 21.41 1.79 0.27 W03 -3.57 0.79 W03 21.08 3.09 0.43 W03 21.15 3.37 0.74 V04 21.17 --21.07 ---3.86 1.47 V04 21.17 --21.20 3.89 1.98 V04 21.69 3.95 4.35 V04 -5.74 2.58 V04 21.64 ---3.18 0.25 W03 20.68 3.28 2.20 W03 21.60 4.92 1.08 V04 21.00 3.53 2.19 V04 -2.55 0.82 V04 20.72 3.27 1.64 W03 -4.33 1.30 V04 21.08 3.68 1.51 V04 -4.17 1.75 V04 21.27 3.23 2.07 W03 -3.58 1.97 V04 -3.30 2.57 W03 -	20.46 -20.60 20.67 20.72 -14.68 -19.32 19.12 17.55 --20.53 20.85 15.68 19.73 20.69 -19.18 -20.00 -15.48 -14.45 21.08 20.20 -20.72 -17.70 19.85 15.02 19.78 19.78 19.61 20.15 -19.82 20.08 20.04 --19.83 19.79 20.24 20.52 -19.77 19.41 20.05 19.89 -20.05 20.68 20.48 -20.57 -18.74 20.71 20.45 -20.65 -20.26 20.92 20.38 ---	0.35 -0.51 0.60 0.82 0.00 -0.06 0.04 0.00 --0.31 0.60 0.00 0.12 0.87 -0.02 -0.08 -0.00 -0.01 0.65 0.21 -0.41 -0.01 0.10 0.00 0.05 0.05 0.04 0.19 -0.08 0.16 0.15 --0.10 0.12 0.16 0.21 -0.09 0.04 0.13 0.12 -0.13 0.38 0.11 -0.15 -0.02 0.21 0.36 -0.63 -0.23 -0.21 ---	A&A 606, A76 (2017)

Table 91

 91

	Interstellar lines extraction summary
		λ 1 -λ 2	Successful fits
		( Å)
	NaI (D 2 -D 1 )	5889.95-5895.92	88
	NaIB (UV doublet) 3302.37-3202.98	78
	CaII (H&K)	3933.66-3968.47	83

  used another approach based on the comparison among pairs of reddened and unreddened standards matching the same spectral type. Most recently, Sonnentrucker et al. (2018) published a modern homogeneous census of the broadest DIBs using a compilation of reddened and unreddened stars of different spectral types.

Table 101

 101 Single-cloud lines-of-sight properties. The 5797/5780 DIB ratio is related to the effective UV radiation field strength; UV exposed environments, σ-type, have ratios < 0.35, while UV-shielded, ζ-type environments, have ratios ≥ 0.35 (c.f.

		Star					ISM
	Identifier	Spectral type v sin(i) (km s -1 ) (mag) E(B-V) a R V	Ref R V	b f(H 2 ) c NaI-UV velocity 5797/5780 d (km s -1 )
	HD 23180	B1 III	100	0.28	3.11 V04	0.51	13.8±0.3	0.73 (ζ)
	HD 24398	B1 Ib	43	0.29	2.63 W03	0.60	14.2±0.2	0.53 (ζ)
	HD 203532 B3 IV	-	0.30	3.37 V04	0.84	15.2±0.2	0.48 (ζ)
	HD 149757 O9.2 IVnn	303	0.32	2.55 V04	0.63	-14.2±0.5	0.43 (ζ)
	HD 185418 B0.5 IIIn	-	0.42	2.54 V04	0.40	-10.7±0.5	0.30 (σ)
	HD 170740 B2 V	25	0.45	3.01 V04	0.58	-9.9±1.5	0.26 (σ)
	HD 147889 B2 V	100	1.03	3.95 V04	0.62	-6.7±1.0	0.41 (ζ)
	HD 169454 e B1 Ia	49	1.03	3.37 V04	-	-9.9±1.5	0.34 (σ)
	a E(B-V) is based on photometry and intrinsic colors.
	b From Valencic et al. (2004) (V04) and Wegner (2003) (W03).

c From Jenkins (2009). d

Table 102

 102 Gas kinetic temperature T kin , volume density n, column density N, Doppler b parameter, radial velocity v derived from Phillips (2-0) C 2 system for the selected sightlines, and C 2 column density normalized to extinction N(C 2 )/E(B-V).

	2 bands and their best-fitting model

  Table 101 when available from the CDS-Simbad 10. Characterization of DIB profiles website. For example, visual inspection of the stellar HeII 5876 Å line in Fig.101shows that HD 149757, HD 185418 are fast rotators while HD 23180, HD 24398, HD 203532, HD 170740 have narrower photospheric lines; in these latter cases, we checked even more carefully for the presence of narrow stellar lines in the DIB spectral regions that could mimic DIBs or DIB substructures (see the cases of4727, 4963, 5541, 5546, 5762, 6729; Table3from[START_REF] Słyk | A search for fine structure inside high resolution profiles of weak diffuse interstellar bands[END_REF]). In the case of the 4734, 5170, 5175, and 6729 Å DIBs, we found that the continuum is contaminated by stellar lines that fall just outside the DIB and, as shown in Figs. 103, 104, 105, 106 and 107 these lines have been excluded from the fitted spectral interval during the continuumfitting.

  Table 103 DIB equivalent widths.

	DIB	23180	24398	203532	149757	185418	170740	147889	169454
	( Å)					(m Å)
					C 2 -DIBs
	4363	-a	<0.3	4.3±0.2	1.3±0.4	< 0.4	< 0.2	11.1±0.4	4.4± 0.2
	4727 a 46.7±0.9 38.3±0.7 55.3±1.1	17.0±1.0 35.8±1.0	45.3±1.0	155.4±2.9 95.0±1.1
	4734	2.3±0.8	<0.5	6.5±0.7	<0.6	<4	<0.5	15.2±0.9	3.2±0.4
	4963	12.0±0.8 9.6±0.3	21.2±0.4	3.8±0.6	11.6±0.7	11.2±0.3	51.9±0.9	24.8±1.0
	4969	3.8±0.3	2.5±0.2	2.8±0.2	1.4±0.5	1.5±0.3	2.2±0.3	9.7±0.8	6.4±0.3
	4979	3.0±0.3	2.5±0.3	3.6±0.3	1.3±0.4	1.9±0.3	2.0±0.3	14.1±0.9	3.9±0.3
	4984	6.6±0.3	3.6±0.3	8.2±0.4	2.4±0.5	4.0±0.3	3.6±0.2	24.4±1.1	11.2±0.3
	5003	-a	-a	4.1±0.2	2.6±0.5	-a	-a	5.6±0.5	-a
	5170	4.5±0.4	1.6±0.2	3.2±0.5	1.3±0.5	1.9±0.3	1.9±0.3	11.1±0.8	5.3±0.2
	5175	2.3±0.2	13.5±0.4 7.9±0.3	2.8±0.8	2.3±0.5	6.2±0.5	31.7±1.0	16.1±0.8
	5418	10.3±0.4 5.1±0.3	12.3±0.3	3.7±0.4	5.4±0.4	5.1±0.3	40.8±0.9	16.4±0.3
	5512	7.5±0.3	5.0±0.2	10.6±0.3	2.2±0.4	3.8±0.4	4.5±0.3	18.2±0.5	14.6±0.3
	5541	4.5±0.3	2.1±0.2	4.7±0.2	1.4±0.3	3.3±0.3	1.7±0.2	10.9±0.8	9.3±0.3
	5546	4.0±0.4	2.0±0.3	4.7±0.3	1.5±0.4	1.9±0.3	1.7±0.1	8.2±0.8	5.7±0.3
	5762	4.6±0.3	2.7±0.2	4.1±0.3	2.8±0.5	7.2±0.5	2.3±0.3	11.6±1.0	8.1±0.3
	5769	4.8±0.4	1.5±0.3	5.2±0.3	<2	<4.5	1.2±0.2	20.6±0.8	5.4±0.3
	5793 b 6.3±0.7	5.3±0.6	5.3±0.4	1.7±0.4	3.9±0.5	3.3±0.4	15.8±1.0	8.7±0.3
	6729	4.2±0.6	5.1±1.5	3.5±0.7	<5	<4	<4	15.5±1.5	6.6±1
				New C 2 -DIB candidates
	4737.5 0.8±0.3	<0.4	1.2±0.2	<0.5	<0.5	blend a	3.0±0.7	0.5±0.2
	5547.4 0.7±0.2	0.9±0.2	0.8±0.1	<0.7	1.3±0.2	0.9±0.1	2.5±0.6	2.0±0.3
	5769.8 1.0±0.2	0.7±0.2	0.7±0.2	<1.3	<1.5	0.7±0.1	1.9±0.7	1.8±0.2
					Selected strong DIBs
	5850	31.1±0.6 20.4±0.7 28.0±0.3	10.9±0.6 27.6±0.6	21.0±0.4	64.6±1.0	61.0±0.7
	5797	58.6±1.0 51.4±0.6 53.1±1.1	28.3±0.9 80.3±1.2	61.2±0.9	143.5±2.5 158.4±2.3
	5780	80.0±0.9 97.2±0.8 108.6±0.9 65.6±1.3 266.1±1.8 237.0±1.6 346.9±2.9 463.9±2.2
	6196	12.6±0.6 13.6±0.7 12.7±0.6	8.5±0.7	33.7±2.1	23.7±0.9	34.8±1.6	52.4±0.6
	6234	5.9±0.7	5.9±0.5	4.7±0.3	2.2±1.0	9.1±0.7	11.6±0.4	14.4±1.4	15.1±1.2
	6376	7.8±0.6	7.7±0.6	7.7±0.7	5.0±0.7	17.3±1.2	10.4±0.9	34.1±2.4	20.0±1.5
	6379	37.6±0.6 57.4±0.7 43.2±0.4	15.2±0.7 71.5±0.7	55.6±0.7	82.4±1.8	93.3±1.4
	6614	45.3±0.7 60.2±0.6 57.7±0.6	41.4±1.0 166.3±0.9 120.0±0.6 160.6±2.1 182.6±1.1
	a blended with stellar lines
	b possible telluric residuals

  . R. The VLT-FLAMES Tarantula Survey. IX. The interstellar medium seen through diffuse interstellar bands and neutral sodium // A&A. II 2013. 550. A108. van der Zwet G. P., Allamandola L. J. Polycyclic aromatic hydrocarbons and the diffuse interstellar bands // A&A. V 1985. 146. 76-80.

In astronomy, dust refers to small solid particles, often referred to as "smoke particles". Not to be confused with Philip Pullman's dust, as discussed in His "Dark Materials" trilogy, nor to the household dust.

The Atlas was updated and enlarged in[START_REF] Barnard | A Photographic Atlas of Selected Regions of the Milky Way[END_REF]

This remark is limited to spiral galaxies. In fact, elliptic ones show a deficit of gas and dust.

By 'Metals' astronomers mean all elements except hydrogen and helium.

The gas can also be seen in the circumstellar environment. The analysis of the velocity shift helped separate the interstellar gas from possible circumstellar gas which would have the same velocity as the stars it surrounds.

The list can be found at http://aramis.obspm.fr/mol/index.html and http://www. cv.nrao.edu/ ~awootten/allmols.html

Atoms and diatomic molecules have FWHM < 0.1 Å

Part of the broadening can be due to Doppler splitting

Also called PAH bands

[START_REF] Cernicharo | Infrared Space Observatory's Discovery of C 4 H 2 , C 6 H 2 , and Benzene in CRL 618[END_REF] have detected Benzene in C-rich protoplanetary nebula CRL618 which represents the first detection of an aromatic compound out of solar system.

The discovery of the fullerene family (C n ) followed that of the buckminsterfullerene, the name was shortened to illustrate the fact that the latter is a type of fullerene

http://data.sdss3.org/sas/dr12/apogee/spectro/redux/\r5/stars/l25_6d/ v603/

http://www.wavemetrics.com

http://www.sdss.org/sas/dr14/apogee/spectro/redux/r8/stars/l31c/l31c.2

A catalog of 1.5273 µm Diffuse Interstellar Bands based on late-type stars

This database will be described in Chapter 5

http://www.linea.gov.br/020-data-center/acesso-a-dados-3/

https://pla.esac.esa.int/pla/

http://www.sdss.org/dr12/

http://www.pole-ether.fr/tapas/

Here, the S/N ratio of average spectra increases in each A V group but the S/Ns are decreasing with increasing A V as the stars are fainter and the signal becomes weaker from the periphery to the center

Note that the term single-cloud does not necessarily imply the existence of a unique homogeneous cloud but rather, a very narrow velocity dispersion of matter along the line-ofsight (much less than the instrumental resolution)

Characterization of DIB profiles

In the laboratory, one could not get close to the required density, but with the supersonic jet expansions, collision-free gas flows which is a good analogue for low densities can be created.

For this spectral range, there may be new DIBs but most certainly not in the classical sense, i.e. as the ones currently known are generally accepted to be due to electronic transitions then it would need a rather special molecule to have two electronic states so close in energy. Moreover, as the absorption coefficient scales with frequency this means absorption will be weaker than for UV/vis.

Acknowledgements

Acknowledgements. We deeply thank our referee Katia Ferrière for careful reading of the manuscript and numerous constructive comments. Her corrections and suggestions resulted in significant improvement of the article. L.C. acknowledges doctoral grant funding from the Centre National d'Études Spatiales (CNES). R.L. and A.M.-I. acknowledge support from the Agence Nationale de la Recherche through the STILISM project (ANR-12-BS05-0016-02) and the CNRS PCMI national program. M.E. acknowledges funding from the Region Ile-de-France through the DIM-ACAV project. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France.

Bibliography

Abolfathi B., Aguado D. S., Aguilar G., Allende Prieto C., Almeida A., Tasnim Ananna T., Anders F., Anderson S. F., Andrews B. H., Anguiano B., al. et. The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the Extended Baryon Oscillation Spectroscopic Survey and from the Second Phase of the Apache Point Observatory Galactic Evolution Experiment // ApJS. IV 2018. 235. 42. Ádámkovics M., Blake G. A., McCall B. J. Diffuse Interstellar Bands Toward HD 62542 // ApJ. VI 2005. 625. 857-863. Adams W. S. Some Results with the COUD É Spectrograph of the Mount Wilson Observatory. // ApJ. I 1941. 93. 11. Adamson A. J., Kerr T. H., Whittet D. C. B., Duley W. W. A Search for the 13175-A Infrared Diffuse Band in Dense Environments // MNRAS. VI 1994. 268. 705. Adamson A. J., Whittet D. C. B., Duley W. W. Diffuse interstellar bands in the Taurus dark clouds // MNRAS. IX 1991. 252. 234-245. Ahn C. P., Alexandroff R., Allende Prieto C., Anders F., Anderson S. F., Anderton T., Andrews B. H., Aubourg É., Bailey S., Bastien F. A., al. et. The Tenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Apache Point Observatory Galactic Evolution Experiment // ApJS. IV 2014. 211. 17. Aihara H., Allende Prieto C., An D., Anderson S. F., Aubourg É., Balbinot E., Beers T. C., Berlind A. A., Bickerton S. J., Bizyaev D., Blanton M. R., Bochanski J. J., Bolton A. S., Bovy J., Brandt W. N., Brinkmann J., Brown P. J., Brownstein J. R., Busca N. G., Campbell H., Carr M. A., Chen Y., Chiappini C., Comparat J., Connolly N., Cortes M., Croft R. A. C., Cuesta A. J., da Costa L. N., Davenport J. R. A., Dawson K., Dhital S., Ealet A., Ebelke G. L., Edmondson E. M., Eisenstein D. J., Escoffier S., Esposito M., Evans M. L., Fan X., Femenía Castellá B., Frinchaboy P. M., Ge J., Gillespie B. A., Gilmore G., González Hernández J. I., Gott J. R., Gould A., Grebel E. K., Gunn J. E., Harding P., Harris D. W., Hawley S. L., Hearty F. R., Ho S., Hogg D. W., Holtzman J. A., Honscheid K., Inada N., Ivans I. I., Jiang L., Johnson J. A., Jordan C., Jordan W. P., Kazin E. A., Kirkby D., Klaene M. A., Knapp G. R., Kochanek C. S., Koesterke L., Kollmeier J. A., Kron R. G., Lampeitl H., Lang D., Le Goff J.-M., Lee Y. S., Lin Y.-T., Long D. C., Loomis C. P., Lucatello S., Lundgren B., Lupton R. H., Ma Z., MacDonald N., Mahadevan S., Maia M. A. G., Makler M., Malanushenko E., Malanushenko V., Mandelbaum R., Maraston C., Margala D., Masters K. L., McBride C. K., McGehee P. M., McGreer I. D., Ménard B., Miralda-Escudé J., Morrison H. L., Mullally F., Muna D., Munn J. A., Murayama H., Myers A. D., Naugle T., Neto A. F., Nguyen D. C., Nichol R. C., O'Connell R. W., Ogando R. L. C., Olmstead M. D., Oravetz D. J., Padmanabhan N., Palanque-Delabrouille N., Pan K., Pandey P., Pâris I., Percival W. J., Petitjean P., Pfaffenberger R., Pforr J., Phleps S., Pichon C., Pieri M. M., Prada F., Price-Whelan A. M., Raddick M. J., Ramos B. H. F., Reylé C., Rich J., Richards G. T., Rix H.-W., Robin A. C., Rocha-Pinto H. J., Rockosi C. M., Roe N. A., Rollinde E., Ross A. J., Ross N. P., Rossetto B. M., Sánchez A. G., Sayres C., Schlegel D. J., Schlesinger K. J., Schmidt S. J., Schneider D. P., Sheldon E., Shu Y., Simmerer J., Simmons A. E., Sivarani T., Snedden S. A., Sobeck J. S., Steinmetz M., Strauss M. A., Szalay A. S., Tanaka M., Thakar A. R., Thomas D., Tinker J. L., Tofflemire B. M., Tojeiro R., Tremonti C. A., Vandenberg J., Vargas Maga ña M., Verde L., Vogt N. P., Wake D. A., Wang J., Weaver B. A., Weinberg D. H., White M., White S. D. M., Yanny B., Yasuda N., Yeche C., Zehavi I. The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III // ApJS. IV 2011. 193. 29. Alam S., Albareti F. D., Allende Prieto C., Anders F., Anderson S. F., Anderton T., Andrews B. H., Armengaud E., Aubourg É., Bailey S., al. et. The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III // ApJS. VII 2015. 219. 12. Allamandola L. J., Tielens A. G. G. M., Barker J. R. Polycyclic aromatic hydrocarbons and the unidentified infrared emission bands -Auto exhaust along the Milky Way // ApJ. III 1985. 290. L25-L28. Alves J. F., Lada C. J., Lada E. A. Internal structure of a cold dark molecular cloud inferred from the extinction of background starlight // Nature. I 2001. 409. 159-161.

Anders F., Queiroz A. B., Chiappini C., Santiago B. X., Fernández-Trincado J. G., Meza A., the SDSS-IV/APOGEE Collaboration . The DR14 APOGEE-TGAS catalogue: Precise chemo-kinematics in the extended solar vicinity // ArXiv e-prints. VIII 2017. Bréchignac P., Pino T. Electronic spectra of cold gas phase PAH cations: Towards the identification of the Diffuse Interstellar Bands carriers // A&A. III 1999. 343. L49-L52. Burke J. R., Hollenbach D. J. The gas-grain interaction in the interstellar medium -Thermal accommodation and trapping // ApJ. II 1983. 265. 223-234. Cami J., Bernard-Salas J., Peeters E., Malek S. E. Detection of C 60 and C 70 in a Young Planetary Nebula // Science. IX 2010. 329. 1180.

Cami J., Cox N. L., Farhang A., Smoker J., Elyajouri M., Lallement R., Bacalla X., Bhatt N. H., Bron E., Cordiner M. A., de Koter A. ., Ehrenfreund P., Evans C., Foing B. H., Javadi A., Joblin C., Kaper L., Khosroshahi H. G., Laverick M., Le Petit F. ., Linnartz H., Marshall C. C., Monreal-Ibero A., Mulas G., Roueff E., Royer P., Salama F., Sarre P. J., Smith K. T., Spaans M., van Loon J. T. ., Wade G. The ESO Diffuse Interstellar Band Large Exploration Survey (EDIBLES) // The Messenger. III 2018. 171. 31-36. Cami J., Salama F., Jiménez-Vicente J., Galazutdinov G. A., Krełowski J. The Rotational Excitation Temperature of the λ6614 Diffuse Interstellar Band Carrier // ApJ. VIII 2004. 611. L113-L116. Cami J., Sonnentrucker P., Ehrenfreund P., Foing B. H. Diffuse Interstellar Bands in single clouds: new families and constraints on the carriers. // A&A. X 1997. 326. 822-830. Campbell E. K., Holz M., Gerlich D., Maier J. P. Laboratory confirmation of C 60 + as the carrier of two diffuse interstellar bands // Nature. VII 2015. 523. 322-323. Campbell E. K., Holz M., Maier J. P., Gerlich D., Walker G. A. H., Bohlender D. Gas Phase Absorption Spectroscopy of C+60 and C+70 in a Cryogenic Ion Trap: Comparison with Astronomical Measurements // ApJ. V 2016a. 822. 17. Campbell E. K., Holz M., Maier J. P., Gerlich D., Walker G. A. H., Bohlender D. Gas Phase Absorption Spectroscopy of C+60 and C+70 in a Cryogenic Ion Trap: Comparison with Astronomical Measurements // ApJ. V 2016b. 822. 17. Campbell E. K., Maier J. P. Isomeric and Isotopic Effects on the Electronic Spectrum of {{\rm{C}}} {60}ˆ{+}-He: Consequences for Astronomical Observations of {{\rm{C}}} {60}ˆ{+} // ApJ. V 2018. 858. 36. Capitanio L., Lallement R., Vergely J. L., Elyajouri M., Monreal-Ibero A. Threedimensional mapping of the local interstellar medium with composite data // A&A. X 2017. 606. A65.

Abstract

High obscuration of background stars behind dark clouds precludes the detection of optical diffuse interstellar bands (DIBs) and hence our knowledge of DIB carriers in these environments. Taking advantage of the reduced obscuration of starlight in the near-infrared (NIR) we used one of the strongest NIR DIBs at 15 273 Å to probe the presence and properties of its carrier throughout the nearby interstellar dark cloud Barnard 68. We measured equivalent widths (EW) for different ranges of visual extinction A V , using VLT/KMOS H-band (1.46-1.85 µm) moderate-resolution (R∼4000) spectra of 43 stars situated behind the cloud. To do so, we fitted the data with synthetic stellar spectra from the APOGEE project and TAPAS synthetic telluric transmissions appropriate for the observing site and time period. The results show an increase of DIB EW with increasing A V . However, the rate of increase is much flatter than expected from the EW-A V quasiproportionality established for this DIB in the Galactic diffuse interstellar medium. Based on a simplified inversion assuming sphericity, it is found that the volume density of the DIB carrier is 2.7 and 7.9 times lower than this expected average value in the external and central regions of the cloud, which have n H ≃ 0.4 and 3.5 × 10 5 cm -3 , respectively. Further measurements with multiplex NIR spectrographs should allow detailed modeling of such an edge effect of this DIB and other bands and help clarify its actual origin. A.2 lists the additional observations that will supplement the data discussed here in due course. The R V and A V extinction values are taken (in order of preference) from Valencic et al. (2004), Fitzpatrick & Massa (2007), or Wegner (2003). H i and H 2 column densities are from Jenkins (2009). The interstellar reddening, E(B -V), was computed from (B -V) J colours (taking the average from Tycho-2 and Simbad B -V colours; where Tycho-2 colours were converted to Johnson colours; Mamajek et al. 2002) and the published spectral classifications, adopting intrinsic colours from Fitzgerald (1970).

Appendix A: Survey sample

References for the spectral classifications in Col. 4 of both tables are: M50 (Morgan & Roman 1950); S52 (Sharpless 1952); Chapter 9

Towards a new DIB atlas 9.1 Line-of-sight structure from extracted IS gaseous lines

For most DIB analyses, it is mandatory to have a knowledge of the line-ofsight structure in order to take into account the cloud multiplicity. This is the case for any study of the saturation or skin-effect. This is the reason why we performed the profile-fitting of the neutral sodium and the ionized calcium lines in order to infer the number, velocity shifts and column-densities of the intervening clouds. Such information will allow to know whether the dominant reddening is produced by one main cloud or multiple ones and attribute a weight to the target in our search for the saturation effect (high weight for mono-cloud sightlines, low weight for multi-cloud sightlines).

In addition and independently, the spectral information for all targets will allow to locate the absorbing clouds and test the relationship between the reddening and DIB vertical scale heights.

In order to select single-cloud sightlines, we have fitted the NaI 3302.368 and 3202.987 Å UV doublet (hereafter, NaIB) and the NaI 5889.95/5895.92 Å D 2 -D 1 doublet (hereafter, NaI). We have also fitted the ionized calcium CaII, namely K and H lines, at 3933.66 and 3968.47 Å. Due to the intrinsic weakness of the transitions for the NaI UV doublet, there is no or very little saturation and it is easy to determine the number of intervening interstellar clouds with gas columns large enough to produce detectable DIBs. In the case of the 5889.95/5895.92 Å doublet there is a strong saturation and line broadening for many clouds, precluding a clear component separation. Instead, these transitions have been used to check the structure deduced from the UV doublet. We modeled the absorption lines of the doublets as convolved products of Voigt profiles in combination with a polynomial continuum, and we determined the radial velocities of the major components (see, e.g. Puspitarini, Lallement 2012). Such a high quality gives also the possibility to increase our understanding about the origin of the variation of DIB profiles (the slight difference in width from star to star). Since this can be explained by the variation of the rotational/vibrational temperature of the carrier species, modelling these resolved profiles will provide the link between the observed spectral variations and the changes in temperature and thus will allow to derive molecular constants that characterize the carriers (constraints on the size and geometry of the possible carrier species).

Abstract

An unambiguous identification of the carriers of the diffuse interstellar bands (DIBs) would provide important clues to the life cycle of interstellar matter. The so-called C 2 -DIBs are a class of weak bands that fall in the blue part of the optical spectrum and are associated with high column densities of the C 2 molecule. DIB profile structures constrain potential molecular carriers, but their measurement requires high signal-to-noise, high-resolution spectra and the use of sightlines without Doppler splitting, as typical for a single-cloud situation. Spectra from the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) conducted at the Very Large Telescope (ESO/Paranal) were explored to identify single-cloud and high C 2 column sightlines, extract the corresponding C 2 -DIBs and study their strengths and profiles, and to investigate in detail any sub-structures. The target selection was made based on profile-fitting of the 3303 and 5895 Å NaI doublets and the detection of C 2 lines. The C 2 (2-0) (8750-8849 Å) Phillips system was fitted using a physical model of the host cloud. C 2 column densities, temperatures as well as gas densities were derived for each sightline. Eighteen known C 2 -DIBs and eight strong non-C 2 DIBs were extracted towards eight targets, comprising seven single-cloud and one multi-cloud line-of-sights. Correlational studies revealed a tight association of the former group with the C 2 columns, whereas the non-C 2 DIBs are primarily correlated with reddening. We report three new weak diffuse band candidates at 4737.5, 5547.4 and 5769.8 Å. We show for the first time that at least 14 C 2 -DIBs exhibit spectral sub-structures which are consistent with unresolved rotational branches of molecular carriers. The variability of their peak separations among the bands for a given sightline implies that their carriers are different molecules with quite different sizes. We also illustrate how profiles of the same DIB vary among targets and as a function of physical parameters, and provide tables defining the sub-structures to be compared with future models and experimental results.

Contributions: For this work, the entire analysis has been made in the frame of this thesis, at the exception of the C2 line system fitting, i.e. the second paragraph of Sect.10.1.3

Summary and conclusion

We have used the high-quality spectra of the EDIBLES survey to select those sightlines that are characterized by both a high C 2 column and a single Doppler component of interstellar absorption (the single-cloud criterion). To achieve the selection, EDIBLES spectra were corrected for atmospheric lines, exposures of the same target were combined and the spectral intervals co-added to form a unique spectrum. The C 2 selection was based on the detection of the C 2 (2-0) (8750-8849 Å) Phillips system and the single-cloud selection was based on profile-fitting of the NaI UV and optical doublets. Seven targets were selected and studied together with one multi-cloud sightline.

The C 2 Phillips bands were fitted to extract the C 2 column, radial velocity and kinetic temperature as well as the gas volume density for each sightline. The absorption bands of the 18 (Thorburn et al. 2003) C 2 -DIBs were extracted. Their equivalent widths as well as the EWs of eight strong DIBs were measured and are provided with their measurement uncertainties. Three additional C 2 -DIB candidates were detected. The C 2 -DIB EWs were compared with the reddening and the C 2 column by means of a linear regression on both quantities. Two categories of DIBs were derived, with a significant gap between the two. The first category corresponds to those classified as C 2 -DIBs by Thorburn et al. (2003) and the second to the strong DIBs more tightly correlated with the reddening. A particular case is the 5850 Å band which seems to belong to a different family. In the case of the C 2 -DIBs, their EWs are found to be very tightly correlated with C 2 column densities. We interpret this as a consequence of accurate N(C 2 ), high signal-to-noise, a careful selection of mono-cloud sightlines and correction for telluric and stellar features.

Individual profiles are presented for all targets and DIBs. We also co-added the DIB profiles of three different sightlines to enhance the sub-structures. The main result of this study is the presence of sub-structures with two or