Nicolas Biver

D Bockelée-Morvan

M Hofstadter

E Lellouch

M Choukroun

S Gulkis

J Crovisier

F P Schloerb

L Rezac

P Von Allmen

S Lee

C Leyrat

W H Ip

P Hartogh

P Encrenaz

G Beaudin

Long-term monitoring of the outgassing and composition of comet 67P/Churyumov-Gerasimenko with the Rosetta/MIRO instrument

Keywords: Comets, general -Comets, individual, 67P/Churyumov-Gerasimenko -Radio lines, planetary system -Submillimeter

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Introduction

Comets are the most pristine remnants of the formation of the Solar System 4.6 billion years ago. Understanding their composition and the sublimation process of their ices as they heat up approaching the Sun was among of the main objectives of the Rosetta mission of the European Space Agency to the comet 67P/Churyumov-Gerasimenko (67P). Comet 67P is a Jupiterfamily comet that reached perihelion on 13.1 August 2015 UT at 1.24 AU from the Sun. After a ten-year-long journey in the Solar System, the Rosetta spacecraft reached (at a distance to the comet of < 100 km) comet 67P on 6 August 2015, at 3.6 AU from the Sun. It entered into orbit or escorted the nucleus of comet 67P from a 5 to 1400 km distance until 30.5 September 2016 when Rosetta terminated its operations at the surface of the comet, at 3.83 AU from the Sun (Accomazzo et al. 2016(Accomazzo et al. , 2017)).

The Microwave Instrument for the Rosetta Orbiter (MIRO) is a submillimeter radiometer equipped with two continuum chan-A&A proofs: manuscript no. 67pqpmaps6_full

We analyze here dedicated mapping observations acquired by slewing the Rosetta spacecraft. These observations are spread over the two years of the Rosetta mission. They provide information along the three dimensions including the (X,Y) mapping coordinates and along the line of sight (Z). The Z axis data is provided by the spectral line resolved velocity information, obtained with the high resolution spectrometer.

Hundreds of raster maps were acquired with sampling and scanning speed adapted to different objectives: (1) mapping of the nucleus and near coma (e.g., Biver et al. 2015), (2) mapping of the coma with a scanning speed adapted to MIRO acquisition duration or (3) faster mapping of the coma appropriate to VIRTIS-H. In addition, sequences sampling a few positions in the coma (coarse maps) were performed. In Table 1 we list the MIRO maps and coarse maps considered in this paper. A full analysis has been performed for three quarters of them.

Section 2 describes the observations and the data reduction and calibration. Section 3 describes the model used to interpret the data and how the expansion velocity and gas temperature are obtained. Section 4 is a step-by-step description of our analysis of the maps. Section 5 presents a discussion of the errors resulting from modeling approximations. Section 6 discusses some of the individual maps. In Section 7 we make use of nadir observations to derive longitudinal averages of the production rates as a function of the cometocentric latitude. In Sect. 8, we investigate the possible contribution of distributed sources of water in the coma. Section 9 presents a determination of the H 18 2 O/H 17 2 O ratio. Section 10 presents the long-term evolution of the outgassing rates and relative production rate ratios. We also provide the cumulative mass loss of volatiles by the comet. We compare the results with measurements from other instruments. Section 11 concludes with a summary of the results.

The processed data (maps and plots) are contained in the Appendixes.

Observations

The MIRO instrument operates as a single beam submillimeter radio telescope mounted rigidly on the Rosetta spacecraft. The pointing axis is aligned with the +Z axis of the spacecraft as are most of the other remote sensing instruments (Koschny et al. 2007). During the entire Rosetta observation period, MIRO has been operating continuously, mostly in "CTS/Dual Continuum" mode (Gulkis et al. 2015). In this mode, frequency-switched spectra at wavelengths ∼ 0.53 mm are acquired every 30 s, and continuum measurements at 0.53 and 1.6 mm wavelengths are obtained every 50 ms. Spectra are obtained with a Chirp Transform Spectrometer (CTS). In some periods, the data rate available to transmit data to the Earth was limited. The data volume was then reduced by averaging spectra or continuum measurements on longer timescales and eventually smoothing the spectra to reduce the number of channels. The beam width at 560 GHz is 7.5 ′ . As the instrument has a single beam, in order to obtain spatial information it was necessary to slew the whole spacecraft over a larger field of view with a slew rate minimizing smearing during individual integrations times. Values from 15 to 30 ′ per minute were used for MIRO spectral maps.

Table 2 lists the lines of H 16 2 O, H 18 2 O, H 17 2 O, CH 3 OH, NH 3 and CO observed in MIRO spectra. Although carbon monoxide is a relatively abundant species, its observation with MIRO was much more difficult than for the other molecules. It only showed up well in periods of high activity. This is due to the weakness of the rotational lines of this molecule with a small dipole moment, and to the fact that the MIRO spectrometer could only observe the CO J(5 -4) line, a transition between relatively high rotational energy levels.

Table 1 provides the list of the maps analyzed in this paper and their characteristics. Their size ranges from 2 to 30 • (∼4 to 45 km considering the distance to the comet). They were acquired in typically 4 h, which corresponds to about one third of the rotation period of the comet of ≈12 h. Fig. 1 shows examples of scanning schemes with measured line intensities. The maps shown in the Appendix A (Figs. A.1-A.14 for H 18 2 O and Fig. A.15 for H 16 2 O) were interpolated for better visualization. For the analysis, however, we used the spectra at their mean locations.

In addition, discrete point-by-point spatial maps were also obtained without a continuous slewing, but still regularly sampling the coma. These were made in periods of low comet activity.

The geometric data provided with the observations were used to compute the coordinates of each observation and the pointing offset relative to the nucleus in the J2000.0 RA/Dec frame. From the RA/Dec coordinates of the spin axis of the comet (Jorda et al. 2016) and the coordinates of the Sun from 67P provided by the JPL Horizons tool (Giorgini et al. 2018), we computed the projected position angle (PA) in the plane of the sky and the aspect angles (Rosetta-Comet-North-pole and Rosetta-Comet-Sun) for the North pole and the Sun directions. Since all maps were done by scanning along the Sun-comet projected vector (Fig. 1), we rotated all maps in order to have the Sun direction either to the left (PA = 90 • ) or right (PA = 270 • ). All pointing offsets were further converted in kilometers relative to the center of 67P.

Reduction and calibration of the MIRO data

The MIRO f 0 = 562 GHz submillimeter local oscillator frequency is switched by ±∆ f = 5 MHz every 5 s. The 30s CTS spectra consist of the average of three pairs of f + (= (Gulkis et al. 2015). A calibration is performed to determine the gain of the receiver every ∼30 min by observing in a sequence a hot load, a cold load, and the sky, each for 30 s. A motor driven flip mirror mounted on the optical bench provides the switching motion. These calibration data were obtained separately for each local oscillator frequency f + and f -. We used spline fitting over 6 h (continuum mode) to 36 h (spectral mode) to calibrate continuum measurements (separately for each frequency) and spectral data in each sub-band of the CTS. Further information on the calibration may be found in Schloerb et al. (2015).

f 0 + ∆ f ) -f -(= f 0 -∆ f ) frequency switched spectra
Additional corrections have been applied:

Fig. 1. Examples of schemes used to scan 67P's coma with Rosetta/MIRO. Each circle has the projected size of the MIRO beam width (7.5 ′ ) at the distance of the comet and is projected at the pointed position at the center of the 30-s integration. The scales of the X and Y axes are the distance from the cent-re of the comet. Arrows show the direction of scanning and its motion during 30 s. The color provides the H 18 2 O integrated line intensity according to the scale given on the right. Sun projected direction is indicated. Blanks along the sampling correspond to gaps in data during calibrations (once every 30 min).

1. A beam efficiency correction η b = 0.94 to convert antenna temperatures T * A into main beam brightness temperatures T mb = T * A /η b . This value was suggested by Schloerb et al. (2015) and from measurements obtained before flight. The beam was described as a main beam of Gaussian profile with a Full Width at Half-Maximum FWHM [ ′′ ] = 456 × (562.8/ν [GHz]), and an additional error beam containing 3% of the signal, ten times broader. This is within 1% of the values determined from the MIRO flight model on ground with an assumed 1/ν frequency dependency. This beam efficiency correction has been applied to both spectral and continuum measurements. 2. A side band gain correction gc l = 1.12 for spectra obtained in the lower side band ( f < f 0 , i.e. the water lines and one line of CH 3 OH) and gc u = 0.90 for the upper side band (i.e. NH 3 , CO, and two CH 3 OH lines). In previous publications and archived data the side band gain ratio was assumed to be 1.0. It was not precisely measured before flight but deviations by more than 10% were not excluded. When considering the saturated part (channels around -0.8 km s -1 ) of the H 16 2 O line, one expects the intensity T em , measured against the blank sky, to be equal to the intensity T bg -T abs measured in absorption in front of the nucleus thermal radiation (see, e.g., Fig. 2), but this is actually not observed. Indeed, whereas the continuum background temperature T bg = 0.5(g l T bg + g u T bg ) originates from the two side bands and does not depend on the the side band gain ratio g u /g l since g u + g l = 2.0, the line intensity does depend on the side-band ratio, e.g., T em (measured) = g l T em for a line in lower side band. Looking at a large set of saturated H 16 2 O lines around perihelion, Article number, page 3 of 60 A&A proofs: manuscript no. 67pqpmaps6_full

we determine the side band gain g l so that:

T em (measured)/g l = T bg -T abs (measured)/g l is satisfied. We found g l = 0.89 (gc l = 1/g l = 1.12) and g u = 1.11 (gc u = 1/g u = 0.90). We use the same correction for all MIRO bands. After applying the side band correction, saturated H 16 2 O profiles in limb and nadir sounding present consistent intensities at negative velocities, as illustrated in Fig. 3. We note that the CH 3 OH(8 +1 -7 0 E) line, observed in lower side band, is always weaker than expected in comparison to the CH 3 OH(3 -2 -2 -1 E) and CH 3 OH(12 -1 -11 -1 E) lines observed in upper side band. After applying the side band correction, the discrepancy is reduced, although the production rates derived from the different methanol lines suggest that a larger correction would be needed. The 30-s average continuum values were added as ancillary data to each spectrum. They are used to analyze the absorption spectra obtained in nadir viewing (Sect. 7).

Fig. 2. Spectra of H 16

2 O obtained with MIRO on 24 Sept. 2015. The five spectra are 4-min averages, obtained during a sequence where the line of sight moved from limb sounding to nadir viewing, and the background changed from 0 to 200 K. The line is saturated between -1.3 and -0.4 km s -1 where its intensity corresponds to the gas temperature near the spacecraft. Top: before applying the side-band gain correction. Bottom: after applying this correction.

Modeling approach

Before describing the step-by-step process to derive the outgassing pattern and total production for each molecule based on the maps, we describe below the geometrical approach of Fig. 3. Intensity measured at -0.8 km s -1 on saturated H 16 2 O spectra obtained on 24 Sept. 2015 between 7.5 h and 24 h UT while scanning across the nucleus (see example of spectra in Fig. 2). The blue and red dots show the value before and after gain correction, respectively. The jumps before correction correspond to times when MIRO was pointing on the nucleus. Error-bars are only plotted for the uncorrected data.

the problem, the excitation and radiative transfer codes used to compute line profiles and intensities, and how we derive two key parameters for these codes, the gas expansion velocity and temperature.

Definition of parameters and model used

In Table 3 we list all the variables introduced in this paper, their units and definition. We have modeled the comet outgassing as the superposition of an isotropic component and a jet-like component with a Gaussian distribution:

q(α) = q I + q J exp - α 2 σ 2 J , with σ J = θ J 2. ln 2 .
with q I the isotropic production rate per solid angle, q J the peak production rate per solid angle in the jet and θ J the FWHM of this Gaussian jet. Figure 4 illustrates the main geometrical parameters of the jet, represented in this figure as a simple cone.

Excitation and outgassing pattern model

We are using excitation and radiative transfer codes described in previous studies (Biver et al. 2015(Biver et al. , 2012;;Zakharov et al. 2007), which take into account collisions with neutrals and electrons, radiative decay and infrared pumping of the rotational levels. Line opacity is taken into account for the excitation using the escape probability formalism. The rotational populations are derived for each distance r to the nucleus with a time evolution linked to the distance via the radial expansion (r(t) = v exp (r)dt). Collisional excitation depends on the gas temperature T (r) as determined hereafter. The computations start at a radius r = 2 km, and all parameters (relative populations, gas temperature, expansion velocity) are assumed to have the same

σ J = θ J 2 √ ( ln 2) [ • ] FWHM of the (Gaussian) Jet θ pro j [ • ] FWHM of the jet in the X,Y plane θ φ [ • ] FWHM of the jet in colatitude: (jet, Z) plane) θ az [ • ] FWHM of the jet in the plane perpendicular to (jet, Z) (θ az is < θ pro j ) A A(PA, ρ) [mK km
T * A = η B T mb T p T em , T abs [K]
Peak intensity of a line (Rayleigh-Jeans equivalent temperature), T p (v), T p (ρ)

[K] " for a given velocity channel, at a given projected distance T bg

[K] Background nucleus (or dust) (Rayleigh-Jeans) temperature (continuum)

Ω jet [sr] Jet solid angle = 2π exp(-α 2 σ 2 J ) sin(α)dα q q(α) [molec. s -1 sr -1
] Production rate per solid angle; as a function of angle α q i (φ), q nadir [molec. s -1 sr -1 ] " as a function of φ (derived from channel i) or nadir value (φ = 0) q I q I,φ , q I,az [molec. s -1 sr -1 ] " for isotropic contribution; derived along φ angle or perpendicularly (az) q J q J,φ , q J,az [molec. s -1 sr -1 ] " in the jet: peak value; derived along φ angle or perpendicularly (az)

Q, Q total Q(ρ), Q(r) [molec. s -1 ]
Total production rate (for a given molecule); locally as a function of ρ or r Q(r h ) [molec. s -1 ] Heliocentric variation of the total production rate of a given molecule

Q I , Q J [molec. s -1 ]
Production rate in the Isotropic, Jet component:

Q = Q I + Q J
Notes. (a) See Fig. 4 value for r < 2 km as for r = 2 km. For collisions with water molecules, we used total cross-sections of 5 × 10 -14 cm 2 for methanol and water, and 2 × 10 -14 cm 2 for ammonia and CO.

Once the rotational populations are computed, radiative transfer is solved by performing a numerical integration (with adaptive step) along the line of sight and over the main beam solid angle. Optical depth effects are thus fully considered for each transition. The pointing offset relative to the direction of the center of the nucleus is taken into account as well as the contribution of the continuum emission from the nucleus (and hidden background gas), when parts of the nucleus are in the beam. The observed continuum background is adjusted to the observed value.

In the radiative transfer model, a jet-like outgassing pattern is considered by restricting the outgassing in a region defined by a range in θ and φ angles: q = q J if φ 2 < φ < φ 1 and 0 < θ < θ m , where θ (azimuth) and φ (colatitude) are spherical coordinates centered on the comet. The corresponding solid angle is Ω = 2θ m (cos(φ 1 ) -cos(φ 2 )), with θ m < π. When fitting the data, the θ m , φ 1 , φ 2 parameters are adjusted so that this synthetic jet is close to the observed one: same direction, similar jet widths θ φ and θ az in the plane of the sky and along the line of sight, and same solid angle Ω jet . The populations of the rotational levels only depend on the distance to the comet center r.

Determination of the gas expansion velocity

A first estimate of the gas expansion velocity is obtained from line profiles seen in absorption against the nucleus thermal emission in nadir pointing. Examples of these lines are provided in Fig. D.1. The peak of absorption seen on the lines is at the Doppler velocity of the bulk of the molecules moving towards the spacecraft, which is the terminal expansion velocity v exp (r = ∞). Close to the surface the gas is accelerated and v exp (r) is increasing quickly. Rotational lines from high energy levels (CO(5-4) and CH 3 OH(12 -1 -11 -1 E)) are formed closer to the surface (where the gas temperature is higher) and tend to show lower Doppler velocity values at the peak of absorption. To simplify the calculation and limit the number of free parameters we assumed a constant v exp , especially as acceleration is affecting only the very first kilometers from the surface. A moderate variation of about +10% between r = 3 and 10 km is however taken into account when necessary for the high activity phase around perihelion.

The expansion velocity along the line of sight can be different from the gas velocity in the plane of the sky or along the jet axis. We have little constraint on the velocity perpendicular to the line of sight. Some observations (e.g., when the line-of-sight in nadir pointing was close to the jet axis) suggest little variations within the jet. Possibly, v exp could be 10% higher in the densest part of the jet, but this does not affect significantly the retrieved production rates. When performing the inversion of the line profiles, we adjusted iteratively v exp to have a reasonable agreement between the simulated line profile and the feet of the lines, which correspond to molecules moving close to the line of sight (see, e.g., top of Figs 10,11). Values used are provided in Table 4 and plotted in Fig. 5. For comparison the velocity profile adopted by Hansen et al. (2016) and Läuter et al. (2019) for water is shown. Note that we use the same expansion velocity for all molecules as we do not see evidence of difference in velocity (e.g. due to their mass) between molecules, but the lower expansion velocity measured post-perihelion beyond 2 AU could be due to mass load by CO 2 which is more abundant in the coma (Läuter et al. 2019).

Gas temperature profile T (r)

We have estimated the kinetic temperature profile T (r) as a function of the radial distance r from the center of the nucleus using constraints from the rotational temperature of methanol and the peak brightness temperature of the saturated H 16 2 O line.

Rotational temperature of the methanol lines

We used the two rotational lines of methanol observed in the same side band:

CH 3 OH(3 -2 -2 -1 E) and CH 3 OH(12 -1 -11 -1 E).
The third line, CH 3 OH(8 +1 -7 0 E), suffers from calibration issues (see Sect. 2.1), and was not used for this study. As the sampled columns are the same, the rotational temperature T rot (CH 3 OH) 4. These values were used for the inversion of the lines profiles and compute production rates for all molecules. Filled blue dots are for pre-perihelion data and empty black symbols are from post-perihelion observations. The fitted power laws are v exp = 0.94r -0.38 h km s -1 preperihelion and v exp = 1.01r -0.53 h km s -1 post-perihelion, with uncertainties of 0.01km s -1 and 0.03 on the power law index. For comparison the adopted (water) expansion velocity profile of Hansen et al. (2016) is plotted in red dotted line. can be readily derived from the ratio of the intensities of the two lines using well known spectroscopic parameters, namely the energy of the levels, their statistical weights and Einstein coefficients. In the inner coma dominated by collisions, we expect methanol to be at Local Thermal Equilibrium (LTE). For optically thin lines, the rotational temperature should then correspond to the gas temperature. Based on radiative transfer calculations, we verified that these conditions are reached for all the T rot (CH 3 OH) values.

Even at the maximum of activity of the comet, the S/N of the CH 3 OH(12 -1 -11 -1 E) line was still insufficient to obtain a full map of T rot (CH 3 OH). However, by averaging the signals over an angular sector and a range of radial offsets from the nucleus, we were able to derive the radial profile of T rot (CH 3 OH) as a function of offset ρ. For additional spatial information, we divided the lines in five velocity bins corresponding to different velocity directions and distances, providing us with measurements of T rot (CH 3 OH) at different radii r (r = ρ/ cos(ψ) with sin(ψ) = v/v exp , see Fig. 7).

Temperature from the saturated water line

Simulation of line profiles at radial offset ρ show that, when the H 16 2 O line is saturated, the main beam brightness temperature T p (H 16 2 O) at velocity channels v i = +0.5 ± 0.2 km s -1 corresponds to the peak intensity of the line within 1 K, and probes in first approximation the gas temperature at distance ρ. Simulations were made for the range of production rates and radial offsets appropriate to the MIRO observations analyzed in this paper. In addition, both in nadir and limb viewing, the main beam brightness temperature measured in the velocity channel at v i = -v exp should correspond to the gas temperature close to the spacecraft T (∆).

Collisions with electrons may affect the temperature retrievals when collisions between neutrals are low, i.e., when the water production rate is low. For high production rates (q H 16 2 O > (H 16 2 O)(-v exp ) both in emission and absorption. (f) Mean jet colatitude (angle between line of sight and direction of peak outgassing).

T rot (CH 3 OH(3 -2 -2 -1 E),CH 3 OH(12 -1 -11 -1 E)) (see text). (e) Value of T mb
3 × 10 26 molec. s -1 sr -1 ), this is only significant at distances > 100 km. We found that, for lower production rates, water excitation is less affected by electron collisions due to lower electron densities, and T (H 16 2 O) can be only up to 5 K higher than the gas temperature. The effect of collisions with electrons could have been observed in the maps, by an increase of T p (H 16 2 O) at some distance, and also by an increase of the brightness temperature at the channels around v i = -v exp (sampling the spacecraft region). However, the observations do not show any evidence for such enhancement, suggesting a relatively low electron density, consistent with the electron density scaling factor xne = 0.2 that we used (Zakharov et al. 2007). At the end of the mission (30 Sept. 2016, r h = 3.8 AU) the Rosetta Plasma Consortium experiments could measure the ion density between 20 km and the surface of the comet (Heritier et al. 2017). The density roughly follows n = 650/r between r = 16 and 5 km from the center of the comet. From our estimated water production rate (1.2 × 10 24 molec. s -1 in π steradians) and expansion velocity (0.45km s -1 ) determined on 27 September 2016, our modeled (cold) electron density close to the nucleus (Zakharov et al. 2007) (where it is proportional to 1/r) yields n e (r) ≈ 2400/r, using xne = 0.2. This is somewhat larger than the observed value, A&A proofs: manuscript no. 67pqpmaps6_full but low enough to not significantly impact the excitation of water, thus compatible with observations. Unfortunately for higher activity there are few or no (in-situ) RPC observation of ion / cold electron densities in the regions we probe with MIRO observations.

Temperature profile

Corrections were done to convert the above retrieved temperatures into gas temperature radial profiles T (r). Indeed, the main-beam brightness temperature T p (H 16 2 O) corresponds to the Rayleigh-Jeans approximation of the black-body radiation, which is a little lower than the black-body temperature. The correction to apply to get a physical temperature at the distance r, taking into account that part of the signal comes from the sidelobes, is: In the higher density part of the coma we generally get a good fit of the data with the law T (r) = a + b/r, the decrease as 1/r being expected from adiabatic cooling. On the night side, T (H 16 2 O)(r) quickly stops decreasing with 1/r, likely because of inefficient adiabatic cooling (and possibly because of some warming by gas-decoupled hot electrons). So, we get a warmer temperature profile for the low density part of the coma, as can be seen in several temperature maps of Appendix B where the "warm" gas on the night side is more localized in the low density anti-jet direction rather than in the anti-sunward direction.

T (H 16 2 O)(r) ≈ T p (
For analyzing nadir observations, we have limited constraints on the temperature profile along the line of sight. We have assumed a 1/r profile as above, constraining the a and b parameters from two measurements: 1) the temperature near the spacecraft T (∆) which is derived from the H 16 2 O line profile at the velocity channel v i = -v exp (see above); for low activity cases, this value will be an upper limit to T (∆); 2) the other constraint is from the maximum intensity at positive velocities, which is close to the nucleus background temperature T bg and provides a constraint on the gas temperature near the surface.

Conversion of the 3-D maps into 2-D outgassing pattern

Method

The maps of the various molecular lines obtained with the MIRO spectrometer contain 3-D information on the outgassing pattern: 2-D spatial information in the plane of the sky (in X and Y directions defined by the Rosetta platform axis, with the Z axis corresponding to Comet-Rosetta axis), plus Doppler velocity information. This velocity information can be converted into spatial information along the Z axis as the Doppler velocity components of the line are the result of the projection on the line of sight of the velocity vector. We use spherical coordinates defined by the azimuthal angle PA (equal to 0 • along the vertical +Y axis) and the colatitude angle φ (Table 3 and Fig. 4). The various steps undertaken to derive information on the outgassing pattern and outgassing rate of each molecule were the followings: . The empty circles correspond to the entire line profile, while the blue, green, black, red and pink dots correspond to rotational temperatures measured in bins of Doppler velocity of respectively (-V; -0.6V), (-0.6V; -0.2V), (-0.2V; +0.2V), (+0.2V; +0.6V) and (+0.6V; +V), with V being either the average expansion velocity or half the FWHM of the line. Each bin of velocity samples gas either closer (v i < 0) or further (v i > 0) away from the plane of the sky, so at a cometocentric distance r larger than the projected distance ρ: r = ρ cos(ψ) , where sin(ψ) = <v> vexp . These dots are plotted at the corresponding deprojected radii r of ±1.667ρ, ±1.091ρ or ρ.

1. Extract azimuthal cuts of the line intensity from the maps.

We use steps of 2.5 km×15 • (20 • for methanol or ammonia, depending on the signal-to-noise ratio). An example is shown in Fig. 8. 2. Fit a Gaussian profile to the azimuthal variation of the line area A(PA) for each radial distance, to derive the projected main direction of the outgassing (PA jet ), the projected angular width of the jet θ pro j from the FWHM of the Gaussian, the peak line area A p , and the background value A 0 , with:

A(PA) = A 0 + A p × 2 -(2 PA-PA J θ pro j ) 2 , (1)
Then, the average values < PA jet > and < θ pro j > over the different radial distances are computed. 3. Average all the on-nucleus (nadir) integrations for each line when available. We generally select observations with a background continuum level larger than 130-160 K, depending on the average sub-mm continuum level around that date.

When too few integrations on the nucleus are available during the mapping, we consider those obtained between two consecutive maps of the same day or just before or after. The line integrated area measured in absorption is converted into an outgassing rate per solid angle (q nadir = q(φ = 0)). 4. Extract line profiles along the jet axis for a range of radii (step of 2.5 km, r > 2.5 km). We average spectra within PA = PA jet ± 30 • , and within PA = (PA jet + 180 • ) ± 30 • (i.e., in the "night" side). This provides us with information on the distribution of the molecules within the jet as a function of the colatitude φ. Examples are shown in Fig. 9. 5. In this step we do what we will call in the following parts of this paper an "inversion" of the line profile to convert the intensity profile T mb (v i ) into a production rate (per solid angle) profile q(φ). From simulated line profiles obtained by using derived v exp and T (r) for both the jet and the night side (Sect. 3), the extracted line profiles are inverted into 2 O line at channel v i ∼ +0.6 km s -1 as a function of projected distance ρ from the nucleus in the jet (full circles). The empty circles correspond to the T mb value for channels at v i ∼ -v exp , which saturate close to the gas temperature at the distance of the spacecraft. The dotted line shows the adopted gas temperature profile (see text). Bottom panel: Same as above for the direction opposite to the jet direction (i.e., night side), using here channels at +0.45 km s -1 . Higher values a few km from the nucleus can be interpreted as due to a lower efficiency of the cooling of the gas via adiabatic expansion. outgassing rate per solid angle q(φ) as a function of the colatitude angle φ, in the plane defined by the Z-axis and jet main axis (with 0 < φ < 180 • on the jet side and 180 < φ < 360 • on the "night" side). For radial flow at a constant velocity v exp , each velocity channel v i of the line corresponds approximately to gas expanding in the direction φ i so that v i = v exp cos(φ i ). Simulations show that, due to thermal broadening, the contribution for each projected velocity spreads over 0.37 to 0.43 km s -1 (FWHM), which is taken into account for the inversion. Angular production rates q i (φ) derived from nearby channels are thus correlated. First order non-linearity due to opacity is also taken into account. The estimated noise on each line channel is used to estimate the uncertainty on the derived values. Figs. 10-12 show the step by step process on one example of spectrum. 6. For each range of radial distances a Gaussian shape is fitted to the q(φ) profiles to derive the following four parameters: q I,φ (background level of the Gaussian, corresponding to the isotropic component of the outgassing), q p (peak value of q(φ)), φ J (direction of the maximum), and θ φ (the FWHM).

An example of the results is shown in Fig. 13. Since the sig- nal was generally averaged over 60 • in azimuth, the true peak outgassing rate needs to be corrected: q J,φ = q p /0.93 for a Gaussian distribution with FWHM ≈ 105 • (the exact correction is computed according to the actual θ pro j ). Then, a weighted (according to error bars) average is computed over the various radii. In the absence of distributed sources of gases or significant time variability the four parameters, q J,φ , q I,φ , φ J and θ φ should not depend on the radii. 7. We can now use < φ J > to correct the effect of projecting a 2-D Gaussian on the plane of the sky. From the derived Gaussian-like profile A(PA) (Eq. 1), and the angle with the line of sight < φ J >, we determined the real width of the Gaussian θ az and the effective contributions A J and A I (A J + A I = A p + A 0 ) of the jet and isotropic components to the signal. We used a code that computes the projection of a 2-D Gaussian and provides the correction factor ǫ needed to compute A J =

A p 1-ǫ . 8. The solid angle Ω jet of the jet is then computed using its average FWHM: θ J = θ φ × θ az . We found values of Ω jet ranging from 0.5 to 1.5π. Uncertainties on the determined θ angles are propagated to Ω jet and to the total production rate in the jet Q J . 9. Given the orientation < φ J > of the jet with respect to the line of sight, we used our 1-D radiative transfer code code to simulate this jet (Sect. 3.2). This allowed us first to check that the mean Doppler shift of the line is well reproduced (especially for weak lines for which the determination of < φ J > was not possible, such as the H 17 2 O line). This simulation is used to convert A J into outgassing rate q J,az . Ω is chosen as close as possible to the measured Ω jet to avoid misestimating q J,az . The comparison with q J,φ generally confirms this. The isotropic component q I,az is also derived from A I .

The assumption that the total outgassing is the sum of two components (jet + isotropic) holds as long as opacity effects are not too high to depart from the linear domain. But in most cases the previous correction step shows that A I is generally much closer to 0 than to A 0 , hence that most of the outgassing is contained in a Gaussian-like jet. In the case of strong opacity effects (e.g., H 16 intensity inside the jet to compute the production rate inside the jet, to avoid non-linearity problems. Averaging over the various radii is then done to get < q I,az > and < q J,az >. 10. The last step is to consider the two perpendicular components (az and φ) to derive the average global values: the peak outgassing rate per solid angle in the jet:

q J = (< q J,az > + < q J,φ >)/2,
the isotropic component:

q I = (< q I,az > + < q I,φ >)/2,
and the corresponding total outgassing rate:

Q = Q I + Q J = 4πq I + Ω jet q J .
The process described above can be done as long as the signal-to-noise ratio allows for H 18 2 O, CH 3 OH and CO, but the line inversion is not simple for NH 3 and H 17 2 O due to their hyperfine structure (Table 5, see examples of spectra in Fig. D.1). Details on the inversion of the NH 3 line will be given later (Sect.4.2).

Values of the total production rates Q, production rates within the Gaussian jet Q J and in the isotropic component Q I are et al. (2009) provided in Table 6. The formal uncertainties are 1-σ: they are based on the weighted average of measurements of q at different nucleus distances, and the (quadratic) sum of the uncertainties on each component (isotropic and jet production rate) and on the jet solid angle. The uncertainty on Q might appear larger than Q itself in some case although the line is clearly detected in the jet part. The decomposition of signal in two contributions from a jet and isotropic outgassing might bring a large uncertainty due to the undetected isotropic contribution in such cases. For low signal-to-noise detections, considering only the jet contribution and its uncertainty can yield a more realistic estimate of the total outgassing rate and its uncertainty.

We provide in Table 7 the derived production rates in the jet Q jetonly obtained when we do not consider any isotropic counterpart. They were obtained by fitting the intensity of the lines along the main jet direction (extractions made in step 4), using the radiative transfer simulations which only consider a jet contribution (Sect. 3.2). Values of Q jetonly have smaller uncertainties than Q values, and their relative uncertainties are more representative of the quality of the detection of the lines. They will be used to compute relative molecular abundances in the jet (Sect. 10).

Line inversion was possible for NH 3 when the line was strong enough (Sect.4.2). When line inversion could not be performed, so that < φ J > values could not be derived (Step 6), we assumed the value of < φ J > determined for H 18 2 O or CH 3 OH(3 -2 -2 -1 E) . For low signal-to-noise data, we observed that considering the isotropic component often added a significant uncertainty on the total outgassing rate, due to its wide (4π) extent. This often resulted in an error bar for the retrieved total production rate Q larger than the value itself. On the other hand, the uncertainties on Q are conservative because Q I and Q J determinations are not independent.

Deconvolution of the ammonia line profile

The ammonia line consists of three hyperfine components, with statistical weights of 1/3, 5/9 and 1/9, which partly overlap (Table 5). In order to perform the retrieval of the jet profile along the φ angle, a deconvolution of the line profile is needed to infer the Doppler profile of NH 3 . This was possible for data acquired from June to October 2015, when the ammonia line was strong enough. We adopted a simplified approach, based on the fact that the F(0 -1) component at -0.953 km s -1 and the F(1 -1) component at +0.650 km s -1 are partly non overlapping with the main F(2-1) hyperfine component. In the simple case of an optically thin line, and a Doppler line width smaller than 1.6 km s -1 , the blue-shifted (non overlapping) part of F(0 -1) component and the red-shifted part of the F(1 -1) component can be used to retrieve the Doppler profile of each single component, as illustrated in the upper plot of Fig. 14. In practice, we retrieved the profile of the strongest (most opaque) F(2 -1) hyperfine component by subtracting the estimated overlapping parts of the weaker components. For broad lines, we used an iterative process to correctly retrieve the contribution of each component to the full line, resulting in a retrieved F(2 -1) line profile with a noise increased by a factor 1.3 to 4.3 depending on the channels. To cope with opacity effects, which affect only the main component, we introduced correcting factors in the iterative process. Fig. 14 illustrates the deconvolution for optically thin and optically thick synthetic spectra. The major caveat of the method is the appearance of a dip in the central part of the line for optically thick cases. However, the line width and position are correctly retrieved and the line area is preserved so that the retrieved outgassing profile should not be significantly affected.

The results of this analysis are presented in Table 6 which provides for each map and molecule the retrieved jet characteristics and production rates. In addition the column densities measured along the jet axis are provided in Appendix C for each day. The column densities are more directly comparable to measurements obtained by other remote instruments like VIRTIS and Alice.

Uncertainties and biases on the retrieved outgassing rates

Smearing of the maps due to long integrations

Maps were acquired in typically 3-4 h (Table 1), during which the nucleus rotated by 90 to 120 • . How does this impact our determination of the properties of the gas jet? If it rotated by this amount, the maps and line profiles do not reflect the effective angular extent of the jet. However, in practice, our analysis is not significantly affected by this effect: (i) the actual time spent to scan the jet itself is less than for the total map; (ii) if the jet is rotating in the plane of the sky, it should appear broader if we scan in the direction of motion, and narrower in the opposite direction: this should compensate on average or show some systematic effects; (iii) as regards the jet transverse profile width deduced from line profile, we use a sub-sample of integrations acquired over an even shorter time scale; (iv) as seen in Table 6, when several consecutive maps of H 18 2 O were reduced separately, the motion of the jet between two maps is generally small (less than 20 • ), so the smearing is not significant. Finally, we find that the outgassing pattern is not instantaneously connected to the surface topography of the nucleus, but rather to the illumination and depends more on the Sun and pole directions, which do not change much during the mapping.

Impact of the uncertainty on the expansion velocity

The terminal expansion velocity v exp is readily determined from the absorption peak in lines against the nucleus: this provides us with the value on the line of sight. For that measurement, the uncertainty is generally below 0.01 km s -1 and has negligible impact on final results. But there is evidence that the expansion velocity varies both radially (v exp (r)) and laterally. As for the radial variation, we could have used a two-parameter law (e.g., as in Biver et al. 2011) but this would only be necessary for the H 16 2 O optically thick line for the contribution at lower velocity close to the nucleus, which is not used for water production determination. Nevertheless, close to perihelion, the "inversion" of the line profiles does require smaller velocities close to the nucleus. In that case we have used different fixed values of v exp (ρ) for projected distances below 5-10 km (e.g. 0.82 km s -1 below 8 km, then 0.85 km s -1 and 0.87 km s -1 beyond 10 km for the inversion of 14 Sept. 2015 line profiles). As for the lateral variation of v exp , we also clearly see that the expansion velocity is higher in the jet closer to sub-solar point than elsewhere. E.g., on 14-26 Sept. 2015, nadir pointing at phase angles (respectively line-ofsight-jet angles) of 103 • (117 • ), 90 • (110 • ) and 54 • (50 • ) yield values of 0.82, 0.84 and 0.90 km s -1 for v exp on the line of sight. So the expansion velocity close to the axis of the jet is higher than is often measured on the line of sight (away from the jet). We tried to constrain v exp from the line profile itself while inverting the line. The derived value (Table 4, Col. 3) is likely lower than the true value in the center of the jet, but more representative of the average velocity v exp needed for a correct line profile inversion. Nevertheless, we have estimated the impact of a 10% error on v exp : an underestimation by 10% results in an overestimation of the jet width θ φ , and to an underestimation of the peak outgassing rates per solid angle q J,φ and q J,az . In the end, the retrieved total production in the jet would be underestimated by 6%. Since it is unlikely that we underestimated v exp by more than 10%, the underestimation of Q is less ∼ 6%.

Impact of the uncertainty on the gas temperature

We used a temperature law consistent with both the derived methanol rotational temperatures and the peak temperatures of the saturated H 16 2 O line. However, there is not always perfect agreement, possibly because their rotational temperatures differ (due to, e.g., collisional excitation effects). The CH 3 OH rotational temperature tends to show deviations from the assumed 1/r profile, with slightly higher values locally e.g. in the r =7-10 km range as seen in Fig. 6. Therefore, we explored the effect of a 20% increase of the temperature along the jet. The test done for the 11.6 Nov. 2015 data shows that the effect is negligible on the derived water production rate (-1.5%) and small for methanol when considering the three lines (+13%). For ammonia, increasing the gas temperature by 20% increases the production rate by +19%. Another test, using 8.7 Sept. 2015 data, where we used instead temperatures higher by ∼ 8%, did not change any of the final production rates by more than 2%. CO is still less affected. We tested for the observation on 30 Aug. (nadir data) -1 Sept. 2015 (map) the effect on the retrieve CO production rate of a temperature profile decreasing from the surface temperature down-to a terminal value between 10 and 200 K (extreme cases, 43 K being the adopted value from Table 4) . For the mapping data, the adopted temperature profile yields the lowest CO production rate, but a factor two in the terminal temperature will not increase the CO production by more than 10%. Only the extreme case of temperature profile would yield a 20-30% increase in CO production. As a conclusion, the derived water and CO production rates do not depend much on the gas temperature, while those of methanol and ammonia could be slightly underestimated if we underestimate the gas temperature. For methanol, the observed behavior is strongly dependent on the lower energy level CH 3 OH(3 -2 -2 -1 E) line (Table 2) which is much better de-tected than the two other transitions and has a larger weight in the computed average production rate. So on average, T+20% implies same Q H 16 2 O and Q CO , Q CH 3 OH +10% and Q NH 3 +20%.

Opacity effects

To perform the inversion of line profiles, opacity is taken into account to first order assuming that the line intensity is proportional to Q 1+x . The coefficient x was determined using the radiative transfer model by computing the line area for two different production rates Q encompassing the observed value.

We had to deal with high opacity effects when analyzing the H 16 2 O line (when H 18 2 O is too faint), or for the H 18 2 O line itself, or even sometimes around perihelion for the NH 3 line. During the line inversion process, we did not consider the channels where optical thickness is so strong that our simplified model fails to reproduce their intensity. This concerned especially channels at negative velocities, i.e., sampling the gas along the line of sight near the spacecraft. Indeed, for large optical depths (τ > 1.0) the intensity starts to depend much more on the local temperature T (r, θ) and velocity gradient than on the column density and a full 2-D accurate modeling of T (r, θ), v exp (r, θ) and n(r, θ) would be necessary.

We note that, by averaging the molecular lines both in time and spatially, local variations of the line opacities due, e.g. to fine, denser structures in the jet, were possibly erased out.

H 16 2 O, H 18 2 O and H 17 2 O have very different optical thickness. To study the H 2 O outgassing properties, we mostly used the H 18 2 O line which, most of the time, is detected and not saturated ( 16 O/ 18 O ∼ 500). Due to its complex hyperfine structure (Table 5), the H 17 2 O line cannot be easily used to invert the line profile. On the other hand, this line is on average 10 times less optically thick for an 18 O/ 17 O isotopic ratio of 5.4. As already discussed, the outgassing rates of H 17 2 O were retrieved using information on the transverse geometry of the outgassing obtained from either H 18 2 O or other molecules. The constancy of the derived Q(H 18 2 O)/Q(H 17 2 O) ratio (Fig. 19, Sect. 9) indicates that opacity effects were well estimated for H 18 2 O.

5.5. 16 O/ 18 O isotopic ratio and water ortho-to-para ratio

For more than 95% of the maps, we used the observation of the ortho line J Ka,Kc = 1 10 -1 01 of H 18 2 O to retrieve the total outgassing rate of water. We have assumed a H 16 2 O/H 18 2 O ratio of 500. This isotopic ratio is close to the Earth value (498.7), and is compatible with values observed in other comets (Biver et al. 2007(Biver et al. , 2016;;[START_REF] Bockelée-Morvan | [END_REF], 2015b). The ROSINA mass spectrometer instrument on board Rosetta and the COSIMA secondary ions mass spectrometer (TOF-SIMS) have measured the 16 Schroeder et al. (2018) and 500 ± 30 in dust grains by Paquette et al. (2018). No observation suggests that this ratio departs by significantly more than 11% from the telluric value, so in the worst case we might underestimate or overestimate by 11% the water outgassing rate for this reason.

We also assumed an ortho-to-para ratio (OPR) of 3. The OPR can be derived from infrared H 2 O spectra where several ro-vibrational lines of ortho and para species are observed simultaneously. Values measured in cometary H 16 2 O range from ∼ 2.5 to ∼ 3.0 (Bockelée- [START_REF] Bockelée-Morvan | Comets II[END_REF]Mumma & Charn-ley 2011). The OPR of H 18 2 O, which has not been yet measured, is presumably similar to that of H 16 2 O. If the OPR is lower, e.g. 2.5, instead of 3, then we underestimate the total production rate of water by a factor (3.5/2.5)/(4/3) = 1.05, e.g. 5%.

Considering the above uncertainties in the 16 O/ 18 O ratio and OPR, in the worst case, we may have underestimated the total outgassing rate of water by 15% or overestimated it by 11% if we used the 16 O/ 18 O measurement of Schroeder et al. (2018)).

Comments on specific days

6.1. 13 Oct. 2014 to 18 Jan. 2015

No coma maps were done between 29 July 2014 and mid-February 2015. Only partial maps of the nucleus with part of the inner coma were executed, such as the one presented in Biver et al. (2015). In order to still retrieve some information on the global outgassing, we have used some observations which provided a enough sampling of the coma around the nucleus to constrain both the azimuthal and transverse extent of the outgassing, in most cases at one or two projected distances from the nucleus. However, the azimuthal sampling can be uneven and/or with varying signal-to-noise ratios, especially for 12/13 Oct. 2014. As a consequence, the determination of the outgassing pattern can be less precise for these dates.

27 Aug. and 01 Sept. 2015

On those dates, during the perihelion period, the south pole and Sun directions were at the closest with respect to the direction of Rosetta: Rosetta-Comet-South pole angle (colatitude angle) of ≈ 46-41 • and Rosetta-Comet-Sun angle (phase angle) of 77-71 • . The bulk of the outgassing was also closer to the spacecraft direction (colatitude ≈ 49 • ) than for the other maps. The observed lines are indeed strongly blue-shifted (∆ v ≈ -0.2 km s -1 ) and the parameters of the jet were adjusted to reproduce as well as possible the observed blue-shifts. In such geometry, the line intensity is much more sensitive to the modeling of the jet pattern, with an opacity of the H 18 2 O line close to 1.0 on average, and 3.0 in the most blue-shifted part of the line, where there is strong self-absorption.

The single nadir pointing observation present in these maps actually yields a high production rate per solid angle on the line of sight of q H 16 2 O ≈ 1.1 -1.7 × 10 27 molec. s -1 sr -1 , based on H 18 2 O and H 17 2 O lines, comparable to the highest value reported by Marshall et al. (2017), and consistent with the value derived from the inversion of the mapping data.

8 Sept. 2015

The two maps were obtained in a relatively fast scanning scheme (5.4 km per spectrum). As a consequence, the projected width of the jet on the plane of the sky is likely a bit overestimated, and indeed the derived width is larger than for other dates. On the other hand, the peak intensity might be underestimated, which compensates for the overestimation of the width in the final computation of the production rate. The jet is also found to be mostly pointing away from the spacecraft (colatitude ≈ 130 • ), which also adds some uncertainty in the azimuthal retrieval of the jet width. In addition to the paucity of nadir pointing data, due to fast scanning and large spacecraft distance, the low outgassing rate towards the spacecraft resulted in the absence of usable absorption lines for this period. Averaging the three maps obtained on this date, all lines but CO are clearly detected with a signal-to-noise ratio warranting retrieval of outgassing pattern information. Averaging CO data on large angular sectors and distance bins yields some marginal detection enabling also line inversion. These maps suggest that the bulk of CO outgassing is in the same direction as other species. Example of spectra and inversion steps are shown in Figs. 8910111213. Interestingly, the width of the jet derived for methanol is line dependent, (Table 6) and is lower when the energy of the upper level of the line transition is higher (Table 2 This suggests that the higher energy levels are more populated in the center of the jet, indicating that the temperature decreases laterally from the axis of the jet. This lateral variation is also seen in the map of the gas temperature derived from the saturated H 16 2 O line (Figs. B.1-B.5),

2 Oct. 2015

The four maps of 2 Oct. 2015 were obtained at one of the largest spacecraft distance (≈ 1200 km) and smallest angle between the line of sight and jet axis (33-45 • ). As a consequence, the H 18 2 O line observed with line-of-sight sampling near-nucleus coma regions has a significant opacity (τ up to 2), and a few degrees change in the direction of the jet can significantly change the line intensity and azimuthal profile. While the transverse (in the Rosetta-comet-jet plane) profile of the outgassing pattern is relatively well constrained, especially for the methanol lines which are optically thin (τ <0.1), the distribution in the plane of the sky is not very well constrained and the derivation of θ az has a large uncertainty or does not converge. The line inversion suggests that the isotropic component (q in the anti-jet direction) is very small (≈ 1/40 of peak q). Therefore, we assumed that its contribution is negligible when performing the retrieval of the azimuthal width of the jet. In order to model the jet (and the opacity effects) as accurately as possible, special care was given to reproduce the observed Doppler shift of the lines (∆ v = -0.19 to -0.37 km s -1 depending on the lines and maps).

22 Nov. 2015

On 22 Nov. 2015, the south pole of the comet is close to the line of sight (colatitude angle less than 30 • ). All lines appear blueshifted (∆ v = -0.1 to -0.4 km s -1 ) and the column densities on the line of sight are large, consistent with a bulk of outgassing close to the line of sight for most molecules. For this geometry, deriving the projected width of the jet on the plane of the sky (and making the de-projection), and extracting the residual signal outside the jet, is tricky. Hence, uncertainties on the effective solid angle of the jet and on the ratio of the isotropic (night) to the jet components of the outgassing are larger. However, the calculation of the total outgassing rate (sum of isotropic + jet outgassing) should be less affected.

3 Dec. 2015

On that day the bulk of the outgassing is clearly moving away from Rosetta (all lines are red-shifted). The colatitude of the CH 3 OH jet (140 • ) is larger than for H 2 O and NH 3 jets (110-120 • ). The derived jet widths (both in azimuth and perpendicularly) are larger than usual, so that part of the outgassing (especially for methanol) is also coming from the night side. Due to large jet colatitude angles, the retrieval of the de-projected azimuthal width of the jet has a high uncertainty.

Constraints on abundances variation with latitude obtained from nadir pointings

Analysis of selected nadir observations

In order to provide further constraints on the outgassing pattern, we analyzed nadir pointings, averaging the data obtained over at least one full rotation of the nucleus. We selected observations with similar sub-spacecraft latitudes (or pole colatitude angles). The mean expansion velocity was derived from the line shapes, and the temperature profile is obtained from the (most often saturated) H 16 2 O line profile, as explained in Sect. 3.4.3. We derived the column density and production rate per unit solid angle q for each line. We performed a weight averaging of the values obtained for the different methanol lines. For water, we used the weighted average of 500 × q(H 18 2 O) and q(H 16 2 O), and 2700 × q(H 17 2 O) when the signal-to-noise ratio is high enough. For H 16 2 O and H 18 2 O , q was estimated both from the line area integrated over channels at negative velocities and the full line area, averaging the two results according to their error bars. Due to imperfect modeling (acceleration close to the nucleus is neglected, but this is not the major source of discrepancy, when testing with a radially variable, expansion velocity model) and the complex structure of the outgassing pattern, the difference between the q derived from the two parts of the line can be very high when the opacity is important (τ > 100). Thus very opaque lines are given a low weight when computing q from these lines. When production rates get very low and H 18 2 O is weakly detected, q H 2 O is mostly based on the main H 16 2 O line.

Results

Computed nadir water production rates (per solid angle) and relative abundances of CH 3 OH, NH 3 and CO are listed in Table 8. We provide the time interval (in most cases averaging variations over nucleus rotation), heliocentric distance, latitude of the subsolar point (0 • at equinoxes) and average colatitude of the north pole φ N , the angle between the Rosetta-comet line and the direction of north pole (sub-spacecraft latitude = 90 -φ N ). Figure 16 shows, for selected time intervals, the abundances versus subspacecraft latitude.

Especially for CO for which mapping data do not give precise values for the jet width and direction, we will use these measurements to obtain rough constraints on the outgassing patterns. The objective is to obtain crude average characteristics: jet width and latitude, over four periods of a few months as listed in Table 9. The jet width and latitude of H 16 2 O are derived from those of H 18 2 O from the mapping data (Figs. 20,21). For each of the selected periods (Table 9) we multiply the derived abundances versus latitude (Fig. 16), by the H 16 2 O Gaussian jet profile (meridian cut) and fit a Gaussian to the result which gives us an estimate of the width and latitude of the Gaussian jet of the considered molecule. The results are provided in Table 9: values in parenthesis are weakly constrained, especially when the jet is close to the pole.

In their analysis of MIRO nadir observations, Marshall et al. ( 2017) derived total production rates assuming uniform outgassing in a 2π solid angle. They multiplied derived nadir production rates per solid angle q nadir by 2π. This yields values which vary strongly with the location of the line of sight relative Article number, page 13 of 60 A&A proofs: manuscript no. 67pqpmaps6_full to the direction of outgassing. To provide more realistic values, we considered the outgassing pattern (jet width and orientation) derived previously for each molecule, and the direction of the line-of sight.

Defining the ratio γ = q I /q J of the angular production rate in the isotropic component to the peak outgassing rate per solid angle in the jet, the total outgassing rate is:

Q total = (4π γ + Ω jet )q J , with q J = q nadir γ + exp(-(θ o f f /σ J ) 2 ) .
θ o f f is the angle between the line-of sight and the jet. The jet is assumed to be at the local noon meridian (phase = 0 • ) and originating from the determined latitude (Table 9). σ J is derived from the jet width:

σ J = θ J / 4 ln(2).
γ can be evaluated from the results in Table 6, but is not very well constrained. We adjusted γ in order to minimize the fluctuations of total production rates obtained with different geometries. Values are in the range 0-20%, more often 5-10%, with γ = 0.07 yielding the most satisfactory results.

The resulting ratio of Q I /Q total is then γ/(γ + Ω jet /4π). However, the value of Q I that can be deduced from this equation has not necessarily a physical meaning, and does not imply that there is water production from the night side. This isotropic contribution is rather a mathematical way to fully describe the gas distribution around the nucleus and derive correct production rates. Indeed, when the S/N ratio enables (e.g. in Biver et al. 2015), we always detect gas on the night side but this may be gas released from the illuminated regions and diffused behind.

The heliocentric evolution of the total production rates derived from nadir observations is shown in Fig. 17.

Distributed sources of water

The presence of a distributed source of water has been proposed to explain the factor of 4 discrepancy between water production rates derived near perihelion from VIRTIS near-nucleus data, and from ROSINA in situ data obtained at cometocentric distances > 200 km (Fougere et al. 2016). There is also a factor of 2.5 discrepancy between the maximum water production rates measured by MIRO (∼ 0.8 × 10 28 molec. s -1 ) and ROSINA ( 2.04 × 10 28 molec. s -1 , Läuter et al. 2019). The MIRO water maps, as well as the comparison of water production rates derived with different field-of-views, provide constraints on the existence of distributed sources of water, such as the sublimation from icy grains, or the decomposition of water dimers and clusters in cometary atmospheres (Crifo & Slanina 1991).

When the maps are sufficiently extended, we can investigate the evolution of the total production rate of water as a function of the projected distance (ρ) from the nucleus. Should water be partly released by some distributed source, we would expect the production rate to increase with increasing ρ.

We have computed production rates for a set of circular annuli (n) covering each ρ = 2.5n to 2.5(n+1) km using the same method as used for the global map (i.e., deriving angular production rates and the jet FWHM in each annulus). This method presents some biases due to (i) the scanning scheme introducing significant smearing for the innermost annuli, (ii) variations of the temperature and gas velocity with the distance to the nucleus, which were however taken into account as much as possible.

Figure 18 shows two of the main significant results. Within error bars, no variation is observed for the derived production rates over the first 20 km from the nucleus: the variation is of +6 ± 5% per 10 km on 20 Aug., and of +7 ± 9% per 10 km on 8 Sept. 2015. A simulation gives a +17% per 10 km (in projected distance) increase in apparent production for a +10% per 10 km (in radial distance) of real increase in production. Based on this, we can conclude that the contribution of icy grains to the total water content at 20 km from the nucleus is less than 15% (3-σ upper limit).

Odin, a submillimeter satellite in Earth orbit (Nordh et al. 2003;Frisk et al. 2003), also observed the H 16 2 O line at 556.936 GHz in comet 67P on 9.47-12.10 Nov. 2015, at a geocentric distance of 1.79 AU. Owing to the 2.1 ′ Odin beam width, the field-of-view was 165 000 km. The line was not detected, with a 3-σ upper limit to the line area of 0.057 K km s -1 . Adopting excitation parameters similar to those used for analyzing the MIRO observations, we derive an upper limit for the water production rate Q H 2 O < 3.3×10 27 molec. s -1 (Snodgrass et al. 2016). The total water production rate estimated from MIRO measurements at the same time (11.5 Nov.) is 2.2 ± 0.1 × 10 27 molec. s -1 . Since the average cometocentric distance sampled by MIRO is on the order of 15 km, the comparison between the two measurements indicates that, if there was significant water production from a distributed source beyond 20 km, then its production rate did not exceed 50% of the value measured by MIRO.

Altogether, our investigation of the contribution of sublimating icy grains to the total water content at 20 km from the nucleus and our comparison with the Odin data suggests that the factor 2.5 discrepancy between the production rates derived from MIRO and ROSINA measurements cannot be explained by sublimating icy grains. A modeling of the line profiles using very accurate velocity, temperature and density profiles is necessary to derive precise isotopic ratios. In addition, the H 17 2 O line occupies most of the spectral window, making it difficult to get an accurate determination of the baseline.

H

Here we computed the H 17 2 O/H 18 2 O ratio by ratioing the production rates (or production rates per solid angle for nadir observations) derived for H 18 2 O and H 17 2 O (Fig. 19). There is significant scatter in the derived H 18 2 O/H 17 2 O in ratios but no significant trend with time (hence with production rate or opacity) is observed. Some higher values are measured, in nadir pointing only, in February 2016, but the detection of H 17 2 O is marginal for these observations, and retrievals are very sensitive to the baseline removal. The average value using all measurements is H 18 2 O/H 17 2 O = 5.45 ± 1.3. A value of 5.6 ± 0.8 is derived when considering only the production rates pertaining to the jet, which have lower uncertainties (square symbols in Fig. 19, with values listed in Table 7). This is compatible with the telluric value (Vienna Standard Mean Ocean Water VSMOW = 5.28) and the ROSINA measurement performed at the beginning of the mission (4.9 ± 1.2 from Altwegg et al. 2015). 9). Isotropic models with Q H 18 2 O = 2 × 10 25 molec. s -1 (blue) and Q H 18 2 O = 4 × 10 25 molec. s -1 (red) are superimposed for channel by channel interpolation and determination of q H 18 2 O for each channel. Central panel: the line intensity in each channel/velocity bin is converted into production rate per solid angle (vertical scale). There is a sharp cut off for absolute velocities that are larger than the expansion velocity, for which the signal is close to 0 as expected. Bottom panel: the horizontal scale in Doppler velocity is converted into colatitude φ angle (v i = v exp cos(φ i )) assuming a constant expansion velocity (v exp = 0.82 km s -1 here). Pink dots are values retrieved from individual channels and connected points are a smoothed profile taking into account the thermal broadening (see text).

Article number, page 15 of 60 • range of PA and [7.5-10] km range of projected radius (mean radius of 8.941 km, middle spectrum from Fig. 9). In the top figure, isotropic models correspond to Q H 18 2 O = 2 × 10 24 molec. s -1 (blue) and Q H 18 2 O = 4 × 10 24 molec. s -1 (red). Fig. 12. Illustration of step 5 of data analysis: polar plot representation of the derived angular production rate q(φ) for the 7.5-10 km projected radius range. The maximum of production (q = 3.5 × 10 24 molec. s -1 sr -1 ) is towards a colatitude angle of ≈ 110 • , slightly away from the direction of Rosetta. In dotted lines are the estimated F(0 -1) (green), F(1 -1) (light blue) and F(2 -1) (red) profiles by the deconvolution routine. The adopted retrieved profile for the F(2 -1) component alone from the deconvolution process is in shown in heavy dark blue -it is the average of the result of the subtraction of the estimated F(0 -1) and F(1 -1) profile to the full line and estimated F(2 -1) (red) profile. Middle panel: Same as above but for Q NH 3 = 22.5 × 10 24 molec. s -1 with strong optical effects which results in slight differences between the retrieved heavy dark blue profile and simulated one in pink. This corresponds to the highest opacity case encountered for this line. Bottom panel: The NH 3 line observed on 30.31-30.65 July 2015 (black), and the deconvolved Doppler profiles of the hyperfine components (same color coding as above plots). The position and relative intensities of the three hyperfine components are indicated below the line, and their identification is indicated above. 6 and7). Triangles refer to nadir measurements of the production rate per unit solid angle, including uncertainties due to the H 17 2 O baseline when specified (larger symbols). The Earth (VS-MOW) value and the value derived from the ROSINA experiment (Altwegg et al. 2015) are shown for comparison.

Long-term evolution of the outgassing

The analysis of the maps presented here provides us with information on the long-term evolution of the outgassing pattern and production rate of the various molecules observed with MIRO. Since the maps were acquired over a few hours, rotational variations are, to some extent, averaged out. Comet 67P exhibited numerous dusty outbursts, especially during the perihelion period (Vincent et al. 2016). However, there is little evidence for significant gas enhancement during outbursts in MIRO data (Marshall et al. 2017;Grün et al. 2016).

Outgassing pattern

Figures 20 and 21 show the time evolution of the source region and width of the jet for the different molecules. The seasonal evolution is clearly seen in these two plots: before the first equinox (10 May 2015, 95 days before perihelion), the outgassing is originating from the northern hemisphere (50 • N), and concentrated in a narrow jet (FWHM ∼ 70-90 • , Fig. 21). The "neck" region is the main source of outgassing during this period, as also observed by other Rosetta instruments (e.g., Migliorini et al. 2016, for VIRTIS). After the first equinox, the activity of 67P moved rapidly to the southern hemisphere. The outgassing also spread over a wider part of the nucleus. The width of the jet reached its maximum at equinox, on the order of 140 • ; in some maps, two jet components were requisite to best fit the spatial distribution (e.g. for methanol on 15 May and 24 June 2015, Table 6).

During the perihelion period, when the gaseous activity was important, the outgassing was originating essentially from the most southern regions, even further south than the sub-solar point, and the jet width was about 110 • . After the second equinox (21 March 2016, 220 days after perihelion), the water outgassing moved back to the northern hemisphere (Fig. 20), while methanol and ammonia continued to be mainly released from southern regions. In the last MIRO maps, obtained later than 250 days after perihelion, all species are originating from the northern hemisphere. Comparing with water and methanol jets, the latitude of the ammonia jet is in general closer to the sub-solar latitude. On the other hand, methanol is always coming from further south than H 2 O and NH 3 . Comparing with ROSINA results, the seasonal evolution of the outgassing of CH 3 OH follows that of CO 2 (Läuter et al. 2019). In Fig. 20, using a degraded version of the nucleus shape model SPG SHAP7 (Preusker et al. 2017) with 125 000 facets, we have also plotted the expected average latitude of the surfaces that receive at least 95% of the maximum solar flux over a 3 days period. As can be anticipated this gives a closer match than the sub-solar latitude to the evolution of the latitude of peak of outgassing, possibly with some delay post-perihelion due to thermal inertia.

Evolution of outgassing and relative abundances in the coma

Figure 22 shows the production rates as a function of heliocentric distance and time, derived from the maps. Results from nadir measurements are presented in Fig. 17. CO production rates derived from nadir measurements have a better S/N ratio, but are marginally lower than those deduced from mapping data. The test done on the 30 Aug. 2015 done actually shows that the nadir derived CO production rate is more sensitive to the temperature profile than the one derived from the maps: increasing by a factor two the terminal CO temperature (compared to the adopted profile for other molecules) would increase by a factor two the retrieved CO production rate and reconcile the CO abundances derived from maps and nadir observations. On the other hand, for the other molecules, nadir-derived production rates are about 30% higher. The nadir measurements are more uncertain, because they rely on our Gaussian jet modeling, which is too simplistic to describe the density distribution along specific line of sights.

We derived power laws (Q(r h ) = Q 0 r β h ) fits for the heliocentric evolution of the production rates, which are shown in Fig. 22. Three periods were considered: pre-perihelion, postperihelion below 1.52 AU, and post-perihelion beyond 1.52 AU. The parameters of the fits are provided in Table 10. The heliocentric variation of the production rates is much steeper (∝ r -5 h to r -8 h ) than observed for long period comets such as C/1995 O1 (Hale-Bopp) (Biver et al. 2002). This steep variation is due to the strong activity of the southern hemisphere during summer time, and points to strong seasonal effects. The heliocentric evolution of the outgassing rates of H 2 O, CO 2 , CO and other species has been studied by Hansen et al. (2016); Gasc et al. (2017) 2019), using ROSINA data. MIRO results are in line with the results of Läuter et al. (2019), who also found steep variations for CO and CO 2 (to which CH 3 OH seems to be linked since they have similar outgassing patterns), but with a lower total abundance of CO (≈ 0.6% versus 2% post-perihelion before equinox) for MIRO. The maximum outgassing rates are measured at the end of August, beginning of September 2015 i.e., about 3 weeks after perihelion, consistent with ROSINA measurements (Hansen et al. 2016).

Figure 23 shows the evolution of the production rate ratios of CH 3 OH, NH 3 , and CO, relative to the water production rate (hereafter, called abundances, as they refer to abundances in the coma). Mapping and nadir measurements are both plotted, and show consistent results. The abundances display clear time variations. In particular, after perihelion, the methanol and CO abundances increase with increasing heliocentric distance, while ammonia becomes less abundant in the coma. As discussed in Sect. 10.1, after perihelion, CH 3 OH mainly outgassed from the southernmost regions, while water production progressively moved from southern to northern regions. The increase of the methanol abundance in the coma after perihelion can be then explained by the sustained activity of the southern hemisphere, whereas the H 2 O activity more rapidly decreased. The similar behavior observed for CO suggests that the southern hemisphere was also over-productive in carbon monoxide. This is consistent with ROSINA measurements, which showed that, whereas the source locations of H 2 O followed the sub-solar latitude, CO 2 sources were mainly located in southern areas during the whole mission, with CO correlating with CO 2 after perihelion (Läuter et al. 2019). Overall, NH 3 correlates with H 2 O as regards the source region (Fig. 20), confirming results obtained from ROSINA (Gasc et al. 2017), and suggesting that NH 3 may be trapped into water ice (e.g., as a trapped species into water amorphous ice, or in the form of an hydrate). The continuous decrease of the NH 3 abundance is surprising, and may suggest that the relative abundance of NH 3 embedded in water ice is higher in the southern hemisphere than in the northern hemisphere. The northern hemisphere is covered by smooth layers of back fall icerich material originating from the southern hemisphere (Keller et al. 2017). This would mean that the falling back chunks lost some of their volatiles during their flight in the coma, as suggested by Fulle et al. (2019).

Comparing the outgassing patterns and heliocentric trends followed by several species (not including CH 3 OH), Gasc et al. (2017) did not find any correlation between outgassing properties and the sublimation temperature of the corresponding pure ices. This result is confirmed by the MIRO results. Indeed NH 3 and CH 3 OH ices have about the same sublimation temperature (respectively 78 and 99 K, Yamamoto 1985), while they behave differently. Gasc et al. (2017) propose that minor species could be partly embedded in CO 2 and partly in H 2 O ices. Their different power laws with heliocentric distance would be therefore indicative for the ratio between how much is embedded in CO 2 and how much is embedded in water.

It is interesting to compare MIRO-derived molecular abundances to those deduced from other instruments. Le Roy et al. (2015), from ROSINA data, derived CH 3 OH/H 2 O abundance ratios in the range 0.31-0.55% at 3.2 AU inbound, where the lower and higher values correspond to the summer (north) and winter (south) hemispheres, respectively. This is a factor of 2 lower than the mean MIRO value for 2.9-3.46 AU inbound. Their derived NH 3 /H 2 O ratio of 0.06-0.15% at 3.2 AU pre-perihelion is also somewhat lower than our value (0.2 %). The discrepancy is still higher post-perihelion. Indeed, Gasc et al. (2017) report a NH 3 /H 2 O value as low as 0.04% for 2.0-2.7 AU post-perihelion, whereas the MIRO average value for this period is 0.1%. The CO/H 2 O ratio derived from ROSINA instruments ranges from ∼ 1 to ∼ 50%, with strong local and temporal variations (Le Roy et al. 2015;Fougere et al. 2016;Hoang et al. 2017;Gasc et al. 2017). A steep decrease from 3.6 to 2.6 AU inbound was observed before it stabilized to a value of about 5% (Fougere et al. 2016). Values measured at heliocentric distances larger than 3.1 AU, inbound and outbound, reach 50%. Läuter et al. (2019) provide the integrated production of water, and CO deduced from ROSINA data, and find a bulk CO/H 2 O abundance of 2.2±0.9%. As shown in Fig. 23, CO/H 2 O ratios measured by MIRO (r h < 2.4 AU) are all below 1%. Measurements with the Alice instrument shortly after perihelion lead to CO/H 2 O ∼ 0.5% (Feldman et al. 2018), in agreement with MIRO determinations. A low (< 1%) CO/H 2 O abundance near perihelion is also consistent with the upper limit measured from infrared spectra obtained by VIR-TIS (Bockelée-Morvan et al. 2016). In summary, there are significant discrepancies between in situ mass spectrometer data and remote sensing spectroscopic data concerning abundance ratios.

It is also interesting to compare the abundances measured in comet 67P to those measured in other comets. This was investigated by Le Roy et al. (2015), using the ROSINA data acquired at 3.2 AU inbound. We here use MIRO data acquired near perihelion, since most comets were observed near their perihelion. Bockelée-Morvan & Biver (2017) present histograms of abundances based on radio measurements of 46 comets. 67P appears to have an ordinary methanol abundance, and does not belong to the methanol-rich group of comets which exhibit abundances relative to water in the range 3 to 6%. The NH 3 abundance of 67P, typically 0.4 % at perihelion, is in the range of values observed for other comets: 0.2-0.7% from radio measurements (Bockelée-Morvan & Biver 2017) and 0.1-1.7% from infrared data (Dello Russo et al. 2016). Comets observed near 1 AU from the Sun display CO/H 2 O production rate ratios in the range 0.2 to 23% (Bockelée-Morvan & Biver 2017). Comet 67P value is in the low end of this range, as for other Jupiter-family comets. This possibly results from a stratification in the ice composition of the subsurface layers of 67P caused by its multiple passages close to the Sun, with the most volatile species only residing in very deep layers.

Bulk abundances and mass losses

We have computed the cumulative mass loss of volatile species during the 2015 apparition of 67P. The results for water and the other molecules observed by MIRO, obtained using linear interpolation between each point, are shown in Figs. 24 and25, respectively. The total mass losses are listed in Table 11. Since CO data are missing beyond 140 days post-perihelion, we assumed that the CO production followed the trend of CH 3 OH in that period. Results obtained using the derived power laws for the heliocentric dependence of the production rates provide similar results (Table 11). Overall, 95% of the volatile mass loss was between the equinoxes, when the southern hemisphere was exposed to the Sun. Ratioing the mass loss rates to that of water, the relative mass loss rates are 1.5 %, 0.34% and 0.6 %, for CH 3 OH, NH 3 and CO, respectively, with a large uncertainty for CO (Table 11). For CO, we adopt a mean abundance of 0.6 ± 0.2% as a compromise between mapping and nadir measurements.

The derived total water loss derived from the mapping data is 2.4 ± 0.1 × 10 9 kg (Table 11), which is a factor of 2-3 below the values derived from ROSINA data: 4.8 ± 1.5 × 10 9 kg (Läuter et al. 2019) and 6.9 ×10 9 kg (Hansen et al. 2016). The values Fig. 22. Heliocentric evolution of the total production rates of water (black, assumed to be 500 × Q H 18 2 O ), methanol (cyan), carbon monoxide (green) and ammonia (magenta) from July 2014 to September 2016, based on mapping data. The horizontal scale is the heliocentric distance (pre-perihelion on the left, post-perihelion on the right). Empty down-pointing triangles correspond to 3-σ upper limits. Dotted lines are power law fits to the heliocentric evolution (see Table 10).

derived from the MIRO nadir data are 17-35% higher than those obtained from mapping data and consistent with the mappingderived value for the power law-fits. The nadir integrated mass loss is probably a bit biased upward by the 5 September value, but the retrieved abundances are fully consistent within errorbars. We note that Marshall et al. (2017) derived a value of 2.4 × 10 9 kg from his analysis of the nadir MIRO data, which is consistent with ours.

In order to evaluate the total mass loss from ice sublimation, one has to consider other molecules, which were not observed by MIRO but are significant contributors to the gas activity: CO 2 , O 2 , sulfur compounds and hydrocarbons. From the VIRTIS data (Bockelée-Morvan et al. 2015a, 2016) and ROSINA measurements (Gasc et al. 2017), the CO 2 abundance showed a behavior somewhat similar to methanol but with an abundance about 16 times higher around perihelion. Calmonte et al. (2016) have estimated that the elemental S/O ratio (in number) in the gas phase is on the order of 0.015. For O 2 , we used the average abundance relative to water of 0.02 (in number) provided by Läuter et al. (2019). Hydrocarbons and other molecules, due to their low abundances (and low molecular masses for the light hydrocarbons), do not significantly contribute to the mass budget (i.e., by less than a few per cent). Adding up all contributions, we find a total ice loss of 4.18 ± 0.18 × 10 9 kg (the uncertainty re- lated to uncertainties in the ortho-to-para ratio and 16 O/ 18 O is at most 11%, and is not considered here). A 28% higher value of 5.36 ± 0.35 × 10 9 kg is derived using nadir data and assuming an OPR of 2.5 to 3.0 and 16 O/ 18 O = 445 ± 35, which is slightly more consistent with ROSINA in situ measurements (Hansen et al. 2016;Läuter et al. 2019). However, as already discussed, we are less confident with this result, given limitations in the modeling approach.

The net mass loss of the nucleus of comet 67P between September 2014 and June 2016 based on Radio Science Investigations (RSI) of the gravity field (Pätzold et al. 2019) is 10.5 ± 3.4 × 10 9 kg, i.e., 0.1% of the nucleus mass, and about one meter of surface erosion (Keller et al. 2017). As a consequence the dust-to-gas ratio -equal or an upper limit of the dust-to-ice ratio -in the material lost by the comet is 1.5 ± 0.8, including all errors. There is evidence for re-deposition, especially in the northern plains, of dusty material (larger particles) (Keller et al. 2017), but the question is how much it represents relative to the mass loss. If the average dust-to-ice ratio of the nucleus significantly differs from what is ejected, it also means that it may no longer be representative to the original value since the nucleus is enriching in refractory / dusty material at each perihelion. E.g., if the pristine dust-to-ice ratio is 8:1, and 2:1 in the lost material, it implies that 0.3% of the nucleus mass in pure dust falls back, budgetary speaking, increasing the global to dust-to-ice ratio (by ∼ 0.018) at every perihelion passage -meaning that it was around 7.5:1 only back to 1963 when 67P reached its present orbit. This puts into question the original dust-to-ice ratio of the comet, especially if it has been active for several centuries as a Jupiter-family comet, after it left the Kuiper Belt. to September 2016. About 40% more mass is lost post-perihelion than pre-perihelion (∼ 1.4 versus 1 million tons), but mid-point (50% cumulative loss) is close to the solstice (vertical red dotted line). 98% of the mass is cumulatively lost within ±200 days from perihelion, and 95% between the two equinoxes (vertical blue dotted lines). The total water loss is 2.42 ± 0.04 × 10 9 kg.

Conclusion

We have presented the analysis of a large data set obtained with the MIRO instrument on board the Rosetta spacecraft. Our study focused on the eight molecular transitions present in the submillimeter spectra. From mapping data of the inner atmosphere of comet 67P, complemented by nadir data, we monitored the geometry of the outgassing and the production rates of H 2 O, CH 3 OH, NH 3 and CO, for a period covering June 2014 to June 2016 (r h = 1.24-3.65 AU). The 3-D spatial distribution of these molecules was deduced by fitting the Doppler velocity profiles and their variation across the coma. We used a simplified modeling, including only measurable parameters.

The main results of this study are:

-The latitudes of the source regions show that water and ammonia outgassing followed approximately the sub-solar point, while methanol was released from the most southern regions during an extended time period after perihelion. At the two solstices, the emission of all molecules originates further pole-ward of the sub-solar latitude. -The spatial distribution of the molecules around the nucleus is anisotropic with more than 90% of the molecules concentrated in a Gaussian-like jet, with a full width at half maximum ranging from 70 to 140 • . The jet width evolves with season, being the broadest around the pre-perihelion equinox and narrower before, when the main source of the outgassing was near the north pole, i.e., the neck areas. -The production rates displayed steep heliocentric variations (∝ r -4 h to r -8 h ), explained by the strong activity of the southern hemisphere when it became illuminated. These strong seasonal effects are probably related to significant erosion in the southern hemisphere, along with accumulation of falling back volatile-poor dust particles in the northern hemisphere.

-The mean abundances in the coma exhibited significant changes near perihelion and during the following months.

Whereas the ammonia to water production rate ratio slightly decreased as the comet receded from the Sun, the CO/H 2 O and CH 3 OH/H 2 O production rate ratios increased. In the range 1.24-3.2 AU post-perihelion, the abundance of methanol increased from 2 to 20%.

Article number, page 23 of 60

A&A proofs: manuscript no. 67pqpmaps6_full

-The CO and CH 3 OH sources are mainly located in southern areas, and seem to correlate with the source regions of CO 2 . The sustained production of these molecules from the most southern regions after perihelion can be explained by sublimation fronts closer to the surface. Indeed, southern regions were strongly eroded. We found that 95% of the mass lost by the nucleus was released between the equinoxes. -There is no correlation between the behavior of NH 3 , CH 3 OH and CO and the sublimation temperature of the pure ices, in line with the conclusions of Gasc et al. (2017) -There is no significant evidence for a distributed source of water, such as sublimating icy grains, within 20 km from nucleus center. Comparing MIRO measurements to remote observations performed with the Odin telescope, we conclude that the factor of 2.5 discrepancy between MIRO and ROSINA water production rates cannot be explained by this process. The contribution of a distributed source of water is at most 50% of the production rate measured by MIRO.

One central question in cometary science is the degree to which molecular abundances in cometary atmospheres are representative of the nucleus internal composition. The MIRO results on 67P activity, combined with those obtained with other Rosetta instruments and thermophysical models, should help solving this issue.

Table 7. Production rates Q j for the jet alone based on derived or assumed jet width and angular peak production rate derived from line intensities along the jet (±30 Notes. (a) Average from maps (Table 6) used to infer values for other molecules.

• ) Date Average Q j[H 16 2 O] Q j [H 18 2 O] Q j [H 17 2 O] Q j [CH 3 OH] Q j [NH 3 ] Q j [
Table 10. Power-law fit β to the production rates (Q 0 r β h ) based on mapping data:

Molecule: H 2 O CH 3 OH NH 3 CO Data based on analysis of mapping data: pre-perihelion:

-5.0 ± 0.1 -5.2 ± 0.5 -5.8 ± 0.3 -9.7 ± 2.6 post-perihelion r h < 1.52 AU: -4.1 ± 0.2 -3.2 ± 0.7 -7.6 ± 0.4 -1.1 ± 2.6 post-perihelion r h > 1.52 AU: -7.9 ± 0.2 -4.5 ± 0.2 -5.7 ± 0.7 -8.9 ± 1.3

Data based on analysis of nadir pointings: pre-perihelion: r h < 2.8 AU -5.0 ± 0.5 -6.8 ± 0.8 -5.0 ± 0.6 -7.6 ± 2.3 post-perihelion r h < 1.7 AU:

-5.3 ± 0.7 -4.5 ± 0.9 -7.8 ± 1.5 -2.4 ± 1.5 post-perihelion r h > 1.7 AU:

-8.4 ± 0.4 -4.9 ± 0.5 -4.2 ± 1.0 -6.3 ± 1.2 Notes. Relative abundances are given by numbers below the mass losses. (a) Includes extrapolation to 3.8 AU pre-and post-perihelion following the behavior of CH 3 OH (i.e. adds 18.7 ± 2.7% mass loss beyond 2 AU from the Sun for mapping data and +2.9% for nadir data).

Article number, page 27 of 60 Notes. (a) Distance Rosetta-comet. (b) Cross-scan across the coma along several radii (at least four every 90 • in PA). (c) Circular coma scan around the nucleus (at 3.5 to 4.5 km projected distance.

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Nicolas Biver et al.: Monitoring of comet 67P with Rosetta/MIRO Table 6. 2-D Gaussian fit to the "Jet" a outgassing pattern and derived production rate Line b "Jet" sky projection "Jet" Rosetta-Jet plane OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) OH(12-11) Notes. (a) The geometrical characteristics of the jet are provided in col. 2-5: Values in parenthesis are assumed (inferred from other molecular lines). (b) CH 3 OH(3-2)= CH 3 OH(3 -2 -2 -1 E); CH 3 OH(8-7)= CH 3 OH(8 +1 -7 0 E); CH 3 OH(12-11)= CH 3 OH(12 -1 -11 -1 E). (c) FWHM of the Gaussian fit in degrees. (d) Solid angle of the jet (integral over 4π of the 2-D Gaussian profile with the mean FWHM θ j taken as geometric average of columns 3 and 5 value). The uncertainty on Ω jet due to uncertainties on the FWHMs of the jet is taken into account to compute uncertainties on the gas production in the jet (columns 8 and 10).

Ω jet d q j [×10 24 ] Q J Q I Q tot PA [ • ] θ az c [ • ] Phase φ [ • ] θ trans c [ • ] [sr] [molec. s -1 sr -1 ] [×10
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Fig. 4 .

 4 Fig. 4. Geometric parameters defining the jet and its appearance on the plane of the sky, defined by the Rosetta X and Y axis. The +Y direction is closest to the North pole and the Sun is either in the +X or -X direction (PA = 90 • or 270 • ).

Fig. 5 .

 5 Fig. 5. Mean gas expansion velocity measured for the jet as given in Table4. These values were used for the inversion of the lines profiles and compute production rates for all molecules. Filled blue dots are for pre-perihelion data and empty black symbols are from post-perihelion observations. The fitted power laws are v exp = 0.94r -0.38

Fig. 6 .

 6 Fig.6. Example of radial profile of the rotational temperature T rot (CH 3 OH) as a function of projected distance ρ from the nucleus in the jet (see text). The empty circles correspond to the entire line profile, while the blue, green, black, red and pink dots correspond to rotational temperatures measured in bins of Doppler velocity of respectively (-V; -0.6V), (-0.6V; -0.2V), (-0.2V; +0.2V), (+0.2V; +0.6V) and (+0.6V; +V), with V being either the average expansion velocity or half the FWHM of the line. Each bin of velocity samples gas either closer (v i < 0) or further (v i > 0) away from the plane of the sky, so at a cometocentric distance r larger than the projected distance ρ: r =

Fig. 8 .

 8 Fig.8. Illustration of step 1 of data analysis: azimuthal cut of the 14.31-14.44 Sept. 2015 UT map of H 18 2 O. The line integrated intensity for lines averaged in bins of 15 • in azimuth (PA scale on the horizontal axis) and 2.5 km in projected radius is plotted for each PA and with a different color for each range of radial distance. The projected directions of the Sun, north pole (blue) and south pole (magenta) are indicated.

Fig. 9 .

 9 Fig. 9. Illustration of step 4 of data analysis: Top panel: the jet. Extracted spectra of H 18 2 O from the 14.31-14.44 Sept. 2015 UT map: Averaged spectra in the [225-285] • range of PA and by step of ≈2.5 km in projected radius. The horizontal scale is Doppler velocity relative to the nucleus and the vertical scale line intensity in T mb scale. Bottom panel: the night side. Same as above but for the [45 -105] • range of PA.

  Article number, page 12 of 60 Nicolas Biver et al.: Monitoring of comet 67P with Rosetta/MIRO 6.4. 14 Sept. 2015

Fig. 10 .

 10 Fig.10. Illustration of step 5 of data analysis, for the jet. Top panel: extracted spectra of H 18 2 O from the 14.31-14.44 Sept. 2015 UT map, in the [225-285] • range of PA and [7.5-10] km range of projected radius (mean radius of 8.648 km, middle spectrum from Fig.9). Isotropic models with Q H 18 2 O = 2 × 10 25 molec. s -1 (blue) and Q H 18 2 O = 4 × 10 25 molec. s -1 (red) are superimposed for channel by channel interpolation and determination of q H 18 2 O for each channel. Central panel: the line intensity in each channel/velocity bin is converted into production rate per solid angle (vertical scale). There is a sharp cut off for absolute velocities that are larger than the expansion velocity, for which the signal is close to 0 as expected. Bottom panel: the horizontal scale in Doppler velocity is converted into colatitude φ angle (v i = v exp cos(φ i )) assuming a constant expansion velocity (v exp = 0.82 km s -1 here). Pink dots are values retrieved from individual channels and connected points are a smoothed profile taking into account the thermal broadening (see text).

Fig. 11 .

 11 Fig. 11. Same as Fig.10for the night side. Extracted spectra of H 18 2 O from the 14.31-14.44 Sept. 2015 UT map, in the [45-105] • range of PA and [7.5-10] km range of projected radius (mean radius of 8.941 km, middle spectrum from Fig.9). In the top figure, isotropic models correspond to Q H 18 2 O = 2 × 10 24 molec. s -1 (blue) and Q H 18 2 O = 4 × 10 24 molec. s -1 (red).

Fig. 13 .

 13 Fig. 13.Step 6: Combining results of step 5 for each range of projected radius, this figure shows the derived angular production rate q(φ) for the H 18 2 O map obtained on 14.31-14.44 Sept. 2015

Fig. 15 .Fig. 16 .

 1516 Fig. 15. Illustration of step 5 of data analysis for a deconvolved NH 3 line profile. See caption of Fig. 10 for the three upper plots, and caption of Fig. 12 for the bottom plot.

Fig. 17 .

 17 Fig.17. Total production rates of water (black, assumed to be 500 × Q H 18 2 O ) methanol (cyan), carbon monoxide (green) and ammonia (magenta) as a function of heliocentric distance, based on nadir integrations. Empty down-pointing triangles correspond to 3-σ upper limits. The horizontal scale is the heliocentric distance (pre-perihelion on the left, post-perihelion on the right).

Fig. 19 .

 19 Fig. 19. Production rates ratio of H 182 O to H 17 2 O as a function of time. The dots refer to total production rates based on maps. Squares refer only to the ratio of production rates in the jet, which have smaller error bars (data from Table6 and 7). Triangles refer to nadir measurements of the production rate per unit solid angle, including uncertainties due to the H 17 2 O baseline when specified (larger symbols). The Earth (VS-MOW) value and the value derived from the ROSINA experiment(Altwegg et al. 2015) are shown for comparison.

Fig. 20 .

 20 Fig. 20. Evolution of the mean latitude of the bulk of outgassing (jet) of water, methanol and ammonia from July 2014 to September 2016. The red stars show the latitude of the sub-solar point, which crosses the equator at the comet equinoxes, 95 days before perihelion and 220 days after. The green dotted line corresponds to the average latitude of the facets that receive the maximum of solar flux.

Fig. 21 .

 21 Fig. 21. Evolution of the mean angular extent of the bulk of outgassing (jet FWHM) of water, methanol and ammonia from July 2014 to September 2016. Representative values of the jet solid angle extent (non-linear scale) are given on the right.

Fig. 23 .

 23 Fig. 23. Evolution of the abundances relative to water of methanol, ammonia and CO in the coma from July 2014 to September 2016. The larger symbols are based of the global outgassing rates derived from mapping data. Additional, more local measurements from nadir or single lines of sight are shown by smaller symbols in the upper panel. Empty down-pointing triangles correspond to 3-σ upper limits. The bottom panel is a zoom over ±200 days around perihelion.

Fig. 24 .

 24 Fig. 24. Cumulative water mass loss rate of comet 67P from July 2014 to September 2016. About 40% more mass is lost post-perihelion than pre-perihelion (∼ 1.4 versus 1 million tons), but mid-point (50% cumulative loss) is close to the solstice (vertical red dotted line). 98% of the mass is cumulatively lost within ±200 days from perihelion, and 95% between the two equinoxes (vertical blue dotted lines). The total water loss is 2.42 ± 0.04 × 10 9 kg.

Fig. 25 .

 25 Fig.25. Same as Fig.24for methanol, carbon monoxide and ammonia. The vertical dotted lines indicate the times of the equinoxes (blue) and solstice (red). Only 1/4 of the mass of methanol and CO has been cumulatively lost at perihelion time. The emission of methanol does not flatten as quickly as for ammonia. The orbit-integrated mass losses at the last points are 66 ± 3, 25 ± 8 and 8.1 ± 0.2 × 10 6 kg for CH 3 OH, CO and NH 3 , respectively.

Fig

  Fig. A.14. Maps (daily average) of interpolated H 18 2 O line intensity on 12 and 22 April 2016.

Fig. A. 15 .

 15 Fig. A.15. Map of interpolated H 16 2 O line intensity on 29 July 2014.

Table 2 .

 2 Lines observed by MIRO

	Molecule	Transition	frequency [MHz]	Upper level energy [K]
	H 16 2 O H 18 2 O H 17 2 O CH 3 OH CH 3 OH CH 3 OH NH 3 CO	1 1,0 -1 0,1 1 1,0 -1 0,1 1 1,0 -1 0,1 3 -2 -2 -1 E 8 +1 -7 0 E 12 -1 -11 -1 E 579150.985 556936.002 547676.440 552021.305 568566.082 553146.224 572498.371 1 0,0 -0 0,1 5-4 576267.933	60.96 60.46 60.70 39.83 104.62 186.43 27.48 82.97

Table 3 .

 3 List of variables

	Symbol Variant	Unit	Description
	Coordinate systems (with origin at the center of the comet nucleus)
	(r, θ, φ) (ρ, θ, z) (X, Y, Z)		[m, • , • ] [m, • ,m] [km, km, km]	Spherical coordinates (radius, azimuth, colatitude) Cylindrical coordinates Cartesian coordinates as defined in Koschny et al. (2007)
	r h ∆			[AU] [km]	Heliocentric distance Distance of Rosetta to the comet
	r ρ T , T rot v exp v	[m or km] [m or km] T (r), T rot (ρ) [K] v exp (r) [km s -1 ] v i [km s -1 ]	Distance to the center of the nucleus Distance to the center of the nucleus in the X,Y mapping plane Gas temperature, rotational temperature; as a function of r or ρ Mean gas expansion velocity; depending on radial distance r Doppler velocity relative to the nucleus; of a spectral channel i
	∆ v α a PA a φ a	[km s -1 ] [ • or radian] PA jet , PA pole [ • ] φ J , φ i , φ pole [ • ]	Mean Doppler shift of a whole line (T mb (v) v dv)/ (T mb (v) dv) angle between the jet axis and considered direction Position angle counted from +Y axis in the (X,Y) map plane Colatitude or polar angle, counted from the -Z axis
	θ J	a

Table 5 .

 5 F 1 Hyperfine components of the H 17 2 O and NH 3 lines observed by MIRO. Notes. I is the relative intensity of the hyperfine component. ∆V is the velocity offset from the line centroid. H 17 2 O data from Puzzarini et al. (2009) and Puzzarini, private communication. NH 3 data from Cazzoli

	F u 2 O 1 10 -1 01 3/2 3/2 552018.458 0.0666 +1.437 I ∆V F l Frequency [MHz] km s -1 H 17 7/2 7/2 552020.137 0.2857 +0.525 3/2 5/2 552021.168 0.1555 +0.035
		5/2 3/2 552021.443 0.1555 -0.185 7/2 5/2 552021.916 0.1587 -0.441 5/2 7/2 552022.381 0.1588 -1.021
		5/2 5/2 552024.159 0.0190 -1.660
	NH 3 (1-0)	1 2 0	1 1 1	572497.130 0.3333 +0.650 572498.371 0.5555 0.000 572500.191 0.1111 -0.953

  O/ 18 O in different materials: 555 ± 62 in H 2 O with DFMS in September 2014 (Altwegg et al. 2015), 445 ± 35 with ROSINA DFMS in H 2 O and OH by

  ; Läuter

	Nicolas Biver et al.: Monitoring of comet 67P with Rosetta/MIRO
	et al. (
	Article number, page 20 of 60

Table 9 .

 9 Notes.(a) Latitude of the sub-solar point.(b) Angle between the North pole and the direction of Rosetta. Molecular jet characteristics retrieved from nadir pointing measurements

	CO]

Table 11 .

 11 Integrated mass losses and relative abundances.

	H 2 O [10 9 kg]	CH 3 OH [10 6 kg]	NH 3 [10 6 kg]	CO [10 6 kg]
	Data based on analysis of mapping data:
	Interpolated: Power law fits: 2.42 ± 0.14 2.42 ± 0.04 100. % Data based on analysis of nadir pointings: 66. ± 3. 8.1 ± 0.2 64. ± 12. 7.8 ± 1.0 1.53 ± 0.08 % 0.35 ± 0.01% 0.75 ± 0.24 % 30. ± 10. a 24. ± 12.
	Interpolated: Power law fits: 2.82 ± 0.67 3.30 ± 0.19 100. %	90. ± 5. 76. ± 21. 1.53 ± 0.13 % 0.34 ± 0.03% 0.31 ± 0.04 % 10.5 ± 0.7 16. ± 1.7 a 8.3 ± 3.4 13. ± 7.6

Table 1 .

 1 Inner coma maps of comet 67P/Churyumov-Gerasimenko

	Date yyyy/mm/dd.dd-dd.dd hh.hh-hh.hh [AU] UT range r h	∆ a [km]	PA	Sun Phase	North Pole PA Phase in X [km] in Y [km] Map Coverage
	2014/07/29.19-29.34 2014/09/07.44-07.60 2014/10/12.77-13.41	4.58-8.12 3.645 1818.5 10.50-14.40 3.410 58.3 18.62-9.55 3.190 18.4	90 • 90 • 90 •	1.5 • 90.4 • 91.7 •	4.2 • 42.8 • 40.7 •	45.1 • 95.6 • 99.0 •	30 5 5	31 5 5 c
	2014/11/23.03-23.33	0.80-7.98 2.920	30.8	90 •	93.8 • 112.4 • 144.5 •	12	11 b
	2014/12/13.55-13.95	13.15-22.76 2.777	19.3	90 •	89.9 •	86.2 • 145.5 •	4.5	8.5 b
	2015/01/01.03-02.01	0.68-24.21 2.643	28.5	90 •	96.2 •	32.0 • 115.4 •	16.8	14.6 b
	2015/01/18.05-18.65 2015/02/15.87-16.07 2015/02/16.13-16.50	1.23-15.64 2.520 20.94-1.76 2.306 151.5 28.5 3.17-11.90 2.303 184.0	270 • 90 • 90 •	93.8 • 321.2 • 135.3 • 90.0 • 23.7 • 91.6 • 90.4 • 23.6 • 91.8 •	18 12.2 15.0	16 b 11.2

Table 1 .

 1 continued.

	Date yyyy/mm/dd.dd-dd.dd hh.hh-hh.hh [AU] UT range r h	∆ a [km]	PA	Sun Phase	North Pole PA Phase in X [km] in Y [km] Map Coverage
	2016/04/22.34-22.52 2016/04/22.58-22.74 2016/04/22.77-22.95	8.20-12.53 2.853 14.00-17.65 2.855 18.48-22.79 2.856	30.8 30.6 30.4	90 • 107.1 • 354.5 • 123.1 • 90 • 109.1 • 351.3 • 127.5 • 90 • 110.7 • 348.0 • 131.2 •	8.3 16.2 16.6	7.5 5.0 13.2

Table 6 .

 6 continued.

	Line b CH 3 OH(3-2) CH 3 OH(12-11) 296 ± 35 124 ± 90 190 ± 40 "Jet" in sky projection "Jet" in Rosetta-Jet plane Ω jet 240 ± 35 190 ± 60 102 ± 4 105 ± 10 1.54π d 188 ± 24 1.70π NH 3 283 ± 3 ± 8 106 ± 2 130 ± 4 1.47π CO 200 ± 30 ± 30 87 ± 16 163 ± 32 1.14π 24.52 Jun. 2015: H 18 2 O 268 ± 2 ± 4 107 ± 1 142 ± 2 1.48π 0.788 ± 0.015 3.67 ± 0.13 1.09 ± 0.19 4.76 ± 0.23 q j [×10 24 ] Q J Q I Q tot 4.13 ± 0.63 20.0 ± 5.7 1.3 ± 5.9 21.3 ± 8.2 6.46 ± 1.71 34.5 ± 15.7 -8.8 ± 13.1 25.7 ± 20.4 1.97 ± 0.07 9.08 ± 0.58 -0.21 ± 0.66 8.9 ± 0.9 7.8 ± 1.4 28 ± 14 -28 ± 14 24.95 Jun. 2015: H 18 2 O 247 ± 2 ± 5 104 ± 1 128 ± 2 1.35π 0.890 ± 0.022 3.78 ± 0.17 1.37 ± 0.16 5.15 ± 0.23 24.74 Jun. 2015:
	CH 3 OH(3-2) 2 nd Jet : CH 3 OH(12-11) 224 ± 28 282 ± 4 140 ± 6 2 nd Jet : (150) NH 3 281 ± 1 CO (150-270) 13.20 July 2015: H 18 2 O 246 ± 2 CH 3 OH(3-2) 234 ± 6 104 ± 15 58 ± 7 ± 12 70 ± 9 (60) ± 3 (122) ± 4 CH 3 OH(12-11) 210 ± 9 ± 15 NH 3 258 ± 2 ± 6 23.38 July 2015: H 18 2 O 237 ± 1 ± 2 CH 3 OH(3-2) 222 ± 4 113 ± 11 CH 3 OH(12-11) 229 ± 10 134 ± 28 NH 3 252 ± 1 ± 4 30.38 July 2015: H 18 2 O 75 ± 1 ± 4 30.57 July 2015: H 18 2 O 75 ± 1 ± 4 30.81 July 2015: H 18 2 O 85 ± 2 157 ± 10 30.59 July 2015: H 17 2 O 74 ± 2 120 ± 11 CH 3 OH(3-2) 76 ± 4 118 ± 15 CH 3 OH(8-7) --CH 3	120 ± 4 102 ± 4 117 ± 20 118 ± 2 109 ± 1 110 ± 6 119 ± 9 115 ± 2 107 ± 1 98 ± 3 121 ± 7 129 ± 2 60 ± 1 54 ± 2 55 ± 3 (55) 34 ± 1 75 ± 8	121 ± 8 105 ± 10 128 ± 38 (60) 127 ± 3 (122) 126 ± 2 119 ± 13 98 ± 17 137 ± 4 132 ± 1 81 ± 9 80 ± 13 111 ± 3 100 ± 2 128 ± 2 124 ± 2 (120) 119 ± 6 128 ± 19	0.68π 0.60π 0.84π 0.37π 1.26π (1.25π) -0.92 ± 1.67 5.95 ± 0.58 6.73 ± 0.62 7.60 ± 1.70 2.0 ± 2.0 2.27 ± 0.05 1.33π 1.410 ± 0.026 5.88 ± 0.21 1.41 ± 0.29 7.29 ± 0.36 12.7 ± 2.4 12.7 ± 3.0 2.1 ± 4.9 27.5 ± 6.2 20.1 ± 7.6 2.4 ± 2.4 16.3 ± 22.6 39. ± 24. 9.0 ± 0.4 1.9 ± 0.6 10.9 ± 0.7 -< 19.7 -3.6 ± 6.6 1.09π 9.72 ± 0.88 33.2 ± 7.2 8.3 ± 6.4 41.6 ± 9.6 0.76π 15.5 ± 2.7 37.0 ± 12.9 8.2 ± 32.8 45.2 ± 35.2 1.48π 3.94 ± 0.12 18.4 ± 1.1 -0.2 ± 1.2 18.2 ± 1.7 1.37π 2.400 ± 0.032 10.31 ± 0.22 0.97 ± 0.38 11.27 ± 0.44 0.85π 15.78 ± 1.26 42.3 ± 7.4 5.0 ± 9.3 47.3 ± 11.9 0.97π 25.64 ± 4.33 78.1 ± 26.3 -7.0 ± 41.8 71.1 ± 49.4 1.33π 5.97 ± 0.12 24.8 ± 1.1 2.7 ± 1.0 27.5 ± 1.5 1.21π 2.569 ± 0.044 9.78 ± 0.35 0.88 ± 0.09 10.66 ± 0.36 1.28π 2.558 ± 0.065 10.25 ± 0.44 1.16 ± 0.13 11.41 ± 0.46 1.52π 2.168 ± 0.041 10.34 ± 0.58 0.68 ± 0.11 11.02 ± 0.59 1.23π 0.343 ± 0.021 1.32 ± 0.19 0.62 ± 0.08 1.94 ± 0.21 1.20π 16.87 ± 0.88 63.5 ± 9.1 -1.7 ± 3.9 61.8 ± 9.9 1.1π 20.38 ± 2.66 70.4 ± 11.2 6.3 ± 37.0 76.7 ± 38.7

Table 6 .

 6 continued.

	NH 3	132 ± 22 ± 15 112 ± 2 ± 5	109 ± 9 98 ± 3	79 ± 15 129 ± 6	0.68π 1.48π	29.2 ± 5.4 5.76 ± 0.18	62.2 ± 22.5 23.6 ± 14.4 85.8 ± 26.7 26.8 ± 1.7 -0.9 ± 0.5 25.8 ± 1.8
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  Table 6. continued.

	Line b 14.61 Sept. 2015: "Jet" in sky projection "Jet" in Rosetta-Jet plane Ω jet H 18 2 O 247 ± 126 ± 2 114 ± 0 96 ± 1 1.07π d 14.40 Sept. 2015: H 17 2 O 240 ± 145 ± 15 (114) (100) 1.23π CH 3 OH(3-2) 238 ± 109 ± 6 113 ± 1 95 ± 2 0.94π CH 3 OH(8-7) 240 ± 10 102 ± 16 114 ± 3 90 ± 7 0.86π CH 3	q j [×10 24 ] 3.42 ± 0.04 0.48 ± 0.02 32.43 ± 0.78 29.36 ± 2.50	Q J 11.5 ± 0.3 1.84 ± 0.29 0.06 ± 0.13 1.90 ± 0.32 Q I Q tot 1.1 ± 0.2 12.6 ± 0.3 96.0 ± 5.9 15.3 ± 4.4 111.3 ± 7.4 79.0 ± 16.0 10.4 ± 23.7 89.4 ± 28.6
	127 ± 20 90 ± 24 106 ± 151 ± 13 130 ± 90 150 ± 70 122 ± 31 128 ± 5 129 ± 3 14.20 Sept. 2015: NH 3 CO H 18 2 O 243 ± 127 ± 2 112 ± 0 14.37 Sept. 2015: H 18 2 O 255 ± 124 ± 2 113 ± 0	87 ± 15 128 ± 4 73 ± 40 105 ± 1 95 ± 1	0.8π 1.51π 1.0π 1.15π 1.04π	35.8 ± 5.9 5.70 ± 0.19 20.0 ± 7.3 3.42 ± 0.03 3.49 ± 0.05	85.5 ± 34.5 -1.9 ± 18.3 83.6 ± 39.1 27.1 ± 2.4 -0.5 ± 0.7 26.6 ± 2.5 62.8 ± 39.2 19.7 ± 26.2 82.5 ± 47.1 12.4 ± 0.2 0.8 ± 0.1 13.2 ± 0.3 11.4 ± 0.3 1.0 ± 0.2 12.4 ± 0.3

  ± 4.90 100.0 ± 43.2 50.0 ± 46.5 150.0 ± 63.5 CH 3

	243 ± 261 ± 265 ± 13 76 ± 28 84 ± 7 122 ± 3 02.00 Oct. 2015: NH 3 CO H 18 2 O 224 ± 115 ± 15 02.23 Oct. 2015: H 18 2 O 221 ± 139 ± 6 02.44 Oct. 2015: H 18 2 O 225 ± 150 ± 30 02.67 Oct. 2015: H 18 2 O 230 ± 145 ± 12 02.33 Oct. 2015: H 17 2 O 220 ± 150 ± 15 CH 3 OH(3-2) 209 ± 110 ± 12 CH 3 OH(8-7) 223 ± 107 ± 40	118 ± 1 123 ± 1 102 ± 13 41 ± 1 46 ± 1 42 ± 1 54 ± 1 (46) 33 ± 2 62 ± 8	92 ± 3 116 ± 2 67 ± 25 119 ± 1 113 ± 1 101 ± 1 117 ± 1 (115) 123 ± 3 115 ± 17	0.74π 1.21π 0.51π 1.17π 1.31π 1.27π 1.38π 1.40π 0.510 ± 0.042 2.24 ± 0.36 -0.08 ± 0.35 2.16 ± 0.50 42.13 ± 1.98 98.1 ± 9.9 7.9 ± 20.1 106.0 ± 22.4 5.25 ± 0.08 20.0 ± 0.6 1.5 ± 0.7 21.4 ± 0.9 15.3 ± 3.7 26.0 ± 17.1 -1.6 ± 25.2 24.4 ± 30.4 2.65 ± 0.09 9.77 ± 1.10 1.1 ± 0.4 10.9 ± 1.2 2.60 ± 0.06 10.7 ± 0.5 0.1 ± 0.2 10.8 ± 0.5 2.59 ± 0.07 10.3 ± 1.6 0.7 ± 0.3 11.1 ± 1.7 2.32 ± 0.06 10.1 ± 0.7 0.9 ± 0.3 10.9 ± 0.8 1.17π 18.33 ± 0.68 67.4 ± 7.3 0.0 ± 6.9 67.4 ± 11.4 1.08π 29.48

  ± 0.15 -0.21 ± 0.08 0.53 ± 0.29 Article number, page 58 of 60 Nicolas Biver et al.: Monitoring of comet 67P with Rosetta/MIRO Table 6. continued. ± 8.5 24.9 ± 17.4 43.8 ± 19.6 CH 3

	118 ± 117 ± 106 ± 10 80 ± 19 77 ± 7 104 ± 4 11.34 Nov. 2015: NH 3 CO H 18 2 O 259 ± 114 ± 2 11.53 Nov. 2015: H 18 2 O 262 ± 123 ± 2 11.73 Nov. 2015: H 18 2 O 270 ± 175 ± 13 11.56 Nov. 2015: H 17 2 O 258 ± 155 ± 48 0.74 Line b 104 ± 3 102 ± 7 0.75π 25.90 ± 1.92 61.2 ± 9.6 16.8 ± 10.2 78.0 ± 14.0 72 ± 2 112 ± 4 1.04π 1.70 ± 0.06 5.54 ± 0.39 1.32 ± 0.77 6.86 ± 0.06 113 ± 16 92 ± 31 0.72π 9.5 ± 3.1 21.5 ± 12.5 -7.5 ± 34.4 14.0 ± 36.6 88 ± 1 104 ± 3 1.05π 1.06 ± 0.03 3.51 ± 0.15 1.04 ± 0.18 4.55 ± 0.23 88 ± 1 106 ± 2 1.13π 1.10 ± 0.02 3.92 ± 0.13 0.43 ± 0.15 4.35 ± 0.20 95 ± 3 87 ± 1 1.27π 1.05 ± 0.03 4.21 ± 0.28 0.18 ± 0.15 4.39 ± 0.32 (90) (90) 1.20π 0.20 ± 0.01 "Jet" in sky projection "Jet" in Rosetta-Jet plane Ω jet d q j [×10 24 ] Q J Q I Q tot CH 3 OH(3-2) 253 ± 3 116 ± 7 112 ± 2 92 ± 3 0.97π 11.24 ± 0.46 34.2 ± 3.2 9.4 ± 4.4 43.6 ± 5.4 CH 3 OH(8-7) 257 ± 12 79 ± 15 101 ± 6 87 ± 20 0.68π 9.32 ± 2.24 19.9

Table 6 .

 6 continued. .015 ± 0.002 0.05 ± 0.01 0.00 ± 0.03 0.05 ± 0.03

	228 ± 7 146 ± 12 105 ± 50 71 ± 13 28.15 Feb. 2016: NH 3 H 18 2 O 115 ± 5 118 ± 13 CH 3 OH(3-2) 142 ± 9 146 ± 30 NH 3 137 ± 23 138 ± 50 14.72 Mar. 2016: H 18 2 O 98 ± 5 86 ± 12 CH 3 OH(3-2) 133 ± 9 62 ± 17	41 ± 6 ( 40 ) 84 ± 2 90 ± 4 (90) 106 ± 3 101 ± 8	72 ± 12 (105) 114 ± 4 111 ± 9 (110) 125 ± 5 126 ± 19	0.52π 1.02π 1.16π 0.059 ± 0.003 0.22 ± 0.03 0.05 ± 0.03 0.27 ± 0.04 5.11 ± 1.15 8.4 ± 3.2 4.9 ± 7.2 13.2 ± 7.8 0.09 ± 0.02 0.28 ± 0.20 -0.06 ± 0.29 0.22 ± 0.35 1.33π 2.11 ± 0.20 8.9 ± 2.0 0.5 ± 2.6 9.4 ± 3.3 (1.25π) 0.04 ± 0.01 0.16 ± 0.06 0.05 ± 0.11 0.21 ± 0.12 0.98π 0.043 ± 0.003 0.13 ± 0.02 0.04 ± 0.04 0.17 ± 0.05 0.76π 1.75 ± 0.31 4.2 ± 1.7 1.9 ± 1.5 6.1 ± 2.2
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O on 29 July 2014) we simply considered the "isotropic" component only outside the jet (in 4π -Ω jet steradians instead of 4π) and used the whole line Article number, page 9 of 60

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Acknowledgements. The authors acknowledge support from their institutions and funding sources. A part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. A part of the research was carried out at the Max-Planck-Institut für Sonnensystemforschung with financial support from Deutsches Zentrum für Luft-und Raumfarht and Max-Planck-Gesellschaft. Parts of the research were carried out by LESIA and LERMA, Observatoire de Paris, with financial support from CNES and CNRS/Institut des sciences de l'univers. A part of the research was carried out at the National Central University with funding from the Taiwanese National Science Counsel grant NSC 101-2111-M-008-016. A part of the research was carried out at the University of Massachusetts, Amherst, USA. We thank Y. Anderson, T. Koch, R. Nowicki, L. Pan, P. Springer and Lucas Kamp for their efforts in scheduling, operations, and support of the MIRO instrument. We specially want to thank all the MIRO engineers, researchers, CoI who participated to the development of the MIRO instrument and passed away since the beginning of the project/mission : we have our best thoughts for Sonia Khatri, Lucas Kamp and Mark Allen from JPL and Maurice Gheudin at Paris Observatory. We thank the Odin Team (A. Sandqvist, S. Lundin, U. Frisk, et al.) for scheduling special observations of comet 67P in support of the Rosetta mission in November 2015. Odin is a Swedish-led satellite project funded jointly by the Swedish National Space Board (SNSB), the Canadian Space Agency (CSA), the National Technology Agency of Finland (Tekes) and the Centre National d'Études Spatiales (CNES, France). The Swedish Space Corporation is the prime contractor, also responsible for Odin operations. The data were reduced and analyzed thanks to the use of the GILDAS, class software (http://www.iram.fr/IRAMFR/GILDAS).

0.56 ± 0.05 0.21 ± 0.02 8.7 ± 2.4 0.20 ± 0.15 5.4 ± 2.8 2016/02/28.2 0.12 ± 0.02 0.24 ± 0.03 -7.9 ± 1.9 < 0.18 -2016/03/14.7 0.08 ± 0.02 0.15 ± 0.02 -5.2 ± 2.0 0.08 ± 0.06 -2016/04/12.7 0.03 ± 0.01 0.08 ± 0.02 -4.9 ± 1.9 0.28 ± 0.15 -2016/04/22.6 0.02 ± 0.01 0.03 ± 0.01 -5.3 ± 2.6 0.08 ± 0.10 -A&A proofs: manuscript no. 67pqpmaps6_full 2 O line which is a proxy (when saturated) for the gas temperature (-10 K due to difference between black-body temperature and Rayleigh-Jeans approximation used for the T mb scale.). In this section we show the evolution of the derived column densities across the night/day side of the coma along the projected axis of the jet. The shaded region corresponds to pointings with the nucleus in the background. Column densities with the nucleus in the background are estimated for an altitude above 2 km from the center of the nucleus. They are very uncertain since density cannot be evaluated precisely for the warm gas close to the surface.