Saher Arnous
email: saher073@gmail.com

Arnaud Leleve
email: arnaud.leleve@insa-lyon.fr

Khalid Kouiss
email: khalid.kouiss@ifma.fr

Patrick Prevot
email: patrick.prevot@insa-lyon.fr

Configuration and Management Tool for Laboratory Training

Electronic Laboratories have been growing during the last decade, but till now, configuring a complex automated system shared between several institutes and used for several disciplines, is a process restricted to qualified staff. Moreover, authoring and managing lab resources (programs, learning scenarios, documentation for both instructors and trainees) is a complex task as soon as the number of instructors, disciplines, different levels… grows up. This paper introduces a software tool aiming at helping in the lab resource management and session configuration.

I.

INTRODUCTION

Laboratory practicals (also called hands-on training), are considered as a key training kind especially in technical and scientific disciplines, through which students can confront their theoretical knowledge with reality [START_REF] Feisel | The Role of the Laboratory in Undergraduate Engineering Education[END_REF]. The way of supporting this training has evolved through years since the end of seventies by getting advantage of the advancement of Computer Technologies and, later, Information and Communication Technologies (ICT). Indeed laboratories have become more and more computerized and qualified as Computer Assisted Lab (also known as Computer Aided Lab [START_REF] Kaiser | Computer aided laboratory instruction[END_REF] or even "ICT aided experiments in real labs") [START_REF] Mani | A study of ICT enabled laboratories[END_REF]. Moreover, they have also evolved into two complementary forms: Virtual Laboratories (V-Labs: experiments by simulation program) and Remote Laboratories (R-Labs: experiments on a real but remote apparatus); in [START_REF] Coquard | AIP-Primeca RAO Remote Laboratories in Automation[END_REF] we introduced the global term "Electronic Laboratories" (E-Labs) to represent all of these enhanced forms of laboratories. ICT provide users with the ability of better controlling operations on real (and virtual) hardware, enabling task automation, data acquisition and archiving, … such as in [START_REF] Consonni | A modern approach to teaching basic experimental electricity and electronics[END_REF][START_REF] Williams | Versatile hardware and software tools for educating students in power electronics[END_REF]. The role and the design of E-Labs in the state of art were widely discussed in the context of distant learning [START_REF] Fujii | A New Remote Laboratory for Hardware Experiment with Shared Resources and Service Management Information Technology and Applications[END_REF][START_REF] U�ur | An internet-based realtime remote automatic control laboratory for control education[END_REF] although Computer Assisted Laboratories are widely used in local learning as well. Indeed, remote manipulation and development of computerized interface for controlling an apparatus are well documented, as in [START_REF] Harward | The iLab Shared Architecture: A Web Services Infrastructure to Build Communities of Internet Accessible Laboratories[END_REF][START_REF] Montes | Universal Laboratory Networking System (ULabNet)[END_REF]. In this paper, we approach the topic of managing and preparing resources for a complex lab session (in this case, in automation discipline, using an Automated Production System (APS). Indeed, configuring such a system, shared between several institutes and used for several disciplines, is a process restricted to qualified staff. Moreover, authoring and managing lab resources (programs, learning scenarios, documentation for both instructors and trainees) is a complex task as soon as the number of instructors, disciplines, different levels … grows up.

The rest of this paper is structured as follows. In the second section we present the context and environment of this project. In the third section we introduce the Configurator tool functioning while, in the fourth section, we depict its implementation and we sum up related experimental results. Finally, we end this paper by a conclusion of the elaborated work and a perspective on future works.

II. CONTEXT AND ENVIRONMENT

In AIP RAO Primeca workshop 1 (a workshop used by several institutes of Rhone Alpes area in order to share high cost software and hardware laboratory resources), we observed that the process of (re)configuring the APS, in order to prepare a hands-on training session, is time consuming. Indeed, a rigorous procedure must be followed: fetching and loading Programmable Logic Controller (PLC) and industrial robot programs, fine-tuning mechanical aspects, filling part stocks, restarting robots and related Automated Systems ... This procedure varies according to training sessions and/or the level of applying trainees.

Moreover, we remarked that this procedure requires a global expertise of handled systems, which often prevents instructors to perform it on their own (for instance, every industrial engineering instructor is not specialist in automation). Thus, because of the small size of teams and their heavy workload, a unique person, in charge of these systems, has acquired this necessary experience. This person performs these preparations when instructors are not autonomous (non-automatician instructors, temporary ones, new ones ...). Besides this person, a part of this expertise is spread among all system users.

To overview the difficulties of preparing handson training sessions, we recently launched a survey targeted at scientist teachers in neighbour universities. The total number of contributors was 47 from several scientific fields where 15% work in automation, 25% in informatics and the rest work in other fields (Industrial Engineering, Electrical Engineering, Mechanical Engineering, Chemistry & Physics, Thermal Engineering and Management Science). This survey (visible in Fig. 1) revealed that 40% of the contributors need between 15 to 60 minutes to prepare a laboratory session and 72% of them require the help of a technician (even in automation 5 contributors declared needing this help).

Moreover in order to write off expensive laboratory resources, they are shared between several colleges, institutes, for various disciplines [START_REF] Dittmar | Multiple use of an industrialdistributed control system in control engineering education Computer Applications[END_REF], and even sometimes, for scientific experiments, as not every university can afford up to date laboratory hardware, not to forget the devoted time for installation, training of the technical staff [START_REF] Rapuano | A learning management system including laboratory experiments on measurement instrumentation[END_REF][START_REF] Kikuchi | Dvts-based remote laboratory across the pacific over the gigabit network[END_REF] and the costs of maintaining and repairing them [START_REF] Langa | Design and implementation of a laboratory practicals management tool[END_REF]. A heavy sharing imposes a shuffled schedule where the configuration of each hardware has to be regularly modified between two sessions. Moreover, their intense usage implies to reduce the required time for this reconfiguration.

Beside these considerations, we also observed that the organization of laboratory resources could be enhanced: each instructor develops his own programs with hardly no feedback to the user community. As they do not have a global view about who programs what and who uses this system for which usage, the experience exchange is difficult.

More generally, for a few years, E-Labs have progressively integrated the use of learning scenarios [START_REF] Kolberg | Lms and interactivity -technical issues for remote laboratories[END_REF] through Learning Management Systems (LMS, such as Moodle2 , for instance). This evolution, combining e-learning support with hands-on activities, is motivated by the need of exchanging and reusing pedagogical resources. As nowadays, laboratory desktops are equipped with computers, the use of a LMS enables instructors to provide online resources more efficiently than traditional printed ones. In the one hand, trainees can directly answer questions and provide their report inside the LMS which facilitates the management of high quantities of trainees and, on the other hand, it enables laboratory learning scenario authors to manage their scenarios with specialized ICT authoring tools and manage the evolution of versions and the trainee reports. On that note, half of contributors of the survey declared being interested by improving the reusability of pedagogical resources and by getting a way to better manage them. In the aforementioned survey, we found that 83% of the contributors reuse their own resources while 63% reuse colleagues' ones and half of them believe that the configurations he/she developed can be reused. In addition to 74% of them think of archiving their configurations where a dedicated library will be useful for this goal (see Fig. 2).

In this context, we propose to automate as many preparation operations as possible, for laboratory sessions in automation discipline, in order to reduce the procedural critical configuration time and to make instructors more autonomous. We built this response through the design of an E-Lab framework, which provides the same materials (programs, learning scenarios, documentations for instructors and trainees ...) as in traditional laboratories, but, in this case, being managed, controlled and maintained through a specific software.

Optimizing the preparation time of laboratory sessions requires to previously organize every required resource (programs, documentation, learning scenarios ...). As a part of these resources is common and reused in different configurations (similar apparatuses/programs, different levels for the same pedagogical objectives, same apparatus and documentation for different disciplines …), we have set the coverage of this software to the root of the process by: managing resource versioning and assembly. Their design is left to specialized software (authoring tool for learning scenarios, automation development tool for PLC programs …). The proposed software acts as an organizer and assembler of, at first, source resources (such as source code, editable files...), and at the end of the process, ready-to-use sets of resources (compiled programs, SCORM packaged learning scenarios, PDF documentation…), which we call "configurations".

III. CONFIGURATOR: ACTORS, REQUIREMENTS AND FUNCTIONAL STRUCTURE

This project capitalizes the experience from two previous projects: ICTT@Lab middleware for adapting generic learning scenarios to apparatuses similar, in terms of functions, but possibly different in terms of hardware [START_REF] Benmohamed | Generic framework for remote laboratory integration[END_REF] and LIMOS methodology for designing IEC 61131-3 function blocks for automation programs [17] using a component-based approach. The following describes the Configurator application through its actors, main functions and process.

A. The actors and main process of the system Configurator tool will be available to both instructor(s) and author(s). It will serve to prepare a laboratory apparatus and the needed resources for hands-on training session. Authors are specialist in their domain (scheduling, robotics, MES 3 ...) but not necessarily specialist in automation. Their role (see Fig. 3) is to prepare configurations ready to use through practical training on some automated system while Instructors' role consists in choosing a configuration according to pedagogical objectives, setting it up and finally using it with trainees. A Technician, specialist in automation, may be charged to develop the required PLC programs and maintain the functioning of the automated system.

Besides these human actors, non-human actors are the APS, the LMS and possibly an E-Lab management system (ElaMS) [START_REF] Benmohamed | Generic framework for remote laboratory integration[END_REF]. The APS is, in 3 MES: Manufacturing Execution System. our case, the main laboratory support resource which requires to be reprogrammed and/or configured at each session change. The LMS is used by Configurator to load corresponding learning scenarios and to deliver them towards instructor and trainees. Finally, the ElaMS is a middleware to manage the communication between LMS and APS during a pedagogical activity. It requires a configuration depending on pedagogical activities and the APS functioning.

B. The main tool functions and data

The tool consists in an Authoring Management Tool (AMT), a Configuration Library and an Installer. The AMT enables the author to create and manage configurations according to pedagogical objectives. This does not include the content authoring (programs, learning scenarios, documentation) itself: this function is left dedicated to specific external existing tools (any authoring tool for learning and documentation design, PLC development suite for PLC programming). So, the author coordinates the resources developed by means of other tools. Authored configurations are stored into the Configuration library. Once chosen by an instructor, these configurations are then loaded on the PLC and LMS by mean of the Installer. It is charged to perform the higher number of automatable operations, the rest of them being manually performed with the help of the appropriate documentation prepared for this reason (see Fig. 4). A configuration is a set of ready to use resources: PLC programs, learning scenario package(s) (in SCORM format) and pieces of documentation (in PDF/Web format) and possibly an ELaMS configuration file.

Data classification hierarchical structures are provided to categorize source elements and to enable keyword based search by indexing them. A first hierarchy defines the pedagogical domain a configuration can belong to ("Automation", "Industrial Engineering"... for instance). In this hierarchy, the children of each domain define more precise topics (PLC programming, Production scheduling…). A second hierarchy is built to define which APS system component a given configuration (or sub element) focuses on. This enables researches from a component point of view and it affords the opportunity to search for Fig. 3. Principal Use Cases and the essential frequent actors. configurations related with RFID or industrial vision (for instance) regardless of aforementioned pedagogical domains. Previous both hierarchies remain generic in the sense that they provide information about tagged elements (documentation, learning scenarios...) independently of a real system. The third hierarchy provides the link between these configurations (and their elements) and a dedicated real system (the Automated Elevator #1 of our lab, the vertical store, …) and their children define more precise links towards subparts of these systems (for instance, station #1, Conveyor #2, HMI, …). Crossing keywords from these three taxonomies enable to retrieve documentations, learning scenarios… for a given apparatus, and (if necessary) more precisely concerning an interesting sub part about a specific topic (Production Scheduling for instance).

C. The main process

Starting from a desired pedagogical objective, instructors search and choose a corresponding configuration from the tool configuration library. The tool then loads corresponding PLC programs on the APS and learning scenario on the LMS. In addition it provides auxiliary documentation necessary for the manual non-automated configuration operations (see Fig. 3).

These configurations are built, each one, from a kind of source configuration (this configuration contains the same resources but in editable form) which we call component. We follow the component based programming paradigm in order to enable and facilitate the reusing of existing resources. A Component represents either a reusable part of a configuration or a final assembled set of resources ready to be compiled into a final configuration. A Component contains only source information (for instance, program pieces stored in OpenPLC format). More precisely, it features links towards related (atomic and previously assembled) source resources to be employed in a practical training session. Components can nest other components and reuse part of their resources (encapsulation and inheritance properties).

D. Indexing atomic and aggregated data

Three ontologies have been built and self expend as new content is added into the software. They represent three points of view to reference each node (document, scenario, component…): "Component perspective", "Real system perspective" and "Pedagogical objectives perspective".

The "Component perspective" provides a hierarchy of automation component terms one can find in an automated system (presence sensor, pneumatic cylinder, PLC…). In term of ontologies, this hierarchy defines a set of classes. It enables to tag nodes to perform component based researches. "Real system perspective" is a hierarchy of tags representing real systems in a workshop and/or their subparts (for example: Loader#1, Vertical store#3, Station#4 of Assembly System#2…). In term of ontologies, this hierarchy defines instances of aforementioned classes, in order to associate a given node with a particular system or subsystem. The "Pedagogical objectives perspective" provides general terms corresponding to learning topics (supervision, scheduling, robotics, etc.). Each term of this list can have on or more references towards terms of "Component perspective" list which gives a wider organized categorization of the created resources. According to this scheme (see Fig. 5), search functions can afford three levels of granularity: specific search according to a given category (created by the user: Loader#1 for instance), component type based search which retrieves all specific resources related to a given component (Loader, Robot arm… for instance) and learning topics based search which retrieves all resources related to a topic and classified according to component types they belong to. Components contain references towards atomic data pieces to enable their sharing between different components. Regarding its functions and data process (multi author content management, versioning, metadata indexing), we opted for a Content Management System (CMS) customized for the configuration needs.

A. Prototype

For the development of a prototype, we chose Drupal4 open source Content Management System (CMS). It is long and widely supported and we already had some experience with it.

Concerning pedagogical content authoring, in order to design LMS-ready learning scenarios (in SCORM format), slides, printed and online documentation by means of a single software, we chose Opale publishing chain software (based on Scenari5 platform) dedicated to realize learning scenarios in addition to the creation of distributable web documentation. For learning scenario delivery, we use Moodle6 LMS as it is the one deployed in our institute but the only constraint is to use a SCORM compliant LMS. PLC programming is performed with Schneider Electric Unity software as our PLC come from this supplier.

We appealed to Content Construction Kit (CCK) module to create different kinds of data: subprogram, piece of documentation, learning scenario element, component and configuration. The source files (for each atomic data type) are stored as attached files with each data node. The taxonomy functions have been used to index each piece of data and enable keyword based searching.

the previous three perspective functions are implemented in the form of three taxonomies. Complementary functions have been added to create clear HMI, to provide a finer versioning for each node, to assemble components to create a hierarchy of them and to generate a configuration from one of these components (the atomic resource compilation and their loading on LMS and PLC are, for this prototype and for time reasons, manually performed by external tools).

B. Experimentation

In order to prove the applicability of this tool, we developed a use case for a few lab sessions on a "Vertical Store" (see Fig. 6). Three different sessions can be performed on this apparatus. Their objectives start from learning how to program an automated system and end on learning how to synchronize two separated automated systems. The operative part of this system features two subsystems: a vertical store allowing storing (manually on one side and automatically on the other one) in pieces. Nacelles rotate inside to transport and present at the door pieces to deliver. Besides, a loader can present and fetch back pieces inside the store and set them on a few parking lots.

Preparing resources

For pedagogical resources, the author designs, by means of Opale software, learning scenarios including demonstration scenarios in addition to the training ones. As many pieces of documentation are common to the three sessions, they are created as 37 different pieces (for three sessions and instructor and trainees) inside Opale and registered in Configurator. Opale enables by itself the reuse of these pieces of documentation when necessary. These documentations are prepared for both of students and instructors with the idea to reuse by reference/inclusion every piece of yet written information in order to prevent rewriting and facilitate their update and the propagation of updates. For the automated system, the author developed required programs, with the help of a technician with the same approach: creating subsections of programs which can be reused in the three sessions.

Building generic configurations

Six configurations were created (Vertical Store and/or Loader, demo or training). Each one shares a number of atomic data pieces (initialization programs, sub-programs for mode management, ..., reference documentation for programming the PLC, system description, current program structure, PLC inputs/outputs, ...). Six different PLC programs are built up from 18 subprograms according to each pedagogical need: Vertical Store Demo (one coupled with and the other one without the loader), Loader Demo (coupled with the vertical store or not). In addition, 3x4 additional documents (such as "System description documentation for student", "Instructions for instructors" …) are also built from 37 pieces.

The time needed to prepare store and loader configurations (around 1 to 1:30 hour considering that there already existed a basis) is longer than a simple copy/paste solution but the gain appears only after this first step when creating a new configuration from existing resources (store + loader: 1h) and when changes occur on pieces of resources so that several configurations are impacted and require to be updated (a few minutes per configuration). With the prototype, as program and learning scenario loading functions were not developed, these steps had to be performed manually. So, no time gain could be recorded yet.

V. CONCLUSION

In this paper we presented a configuration tool to help the author/instructor to prepare a hands-on session configuration on an automated system. The experiment presented in this paper already revealed time gains with a small automated system and very few related configurations. The continuous use of the Configurator should result in a richer library of configurations with different pedagogical objectives which should enhance again the reusing and exchange of resources and, in turn, authoring time gains. Experimentations on a larger automated system featuring more subsystems and configurations with common parts will be performed to test further the gains of such an approach. Later, we think to integrate this tool into the process of training technical staffs of firms supplying automated systems as the core of training cases is not different from the one exposed in this paper.

Fig. 1 .

 1 Fig. 1. Survey results: disciplines, configuration time and help requirement.

Fig. 2 .

 2 Fig. 2. Resource reusability and management.

Fig. 4 .

 4 Fig. 4. Configurator model.

Fig. 6 .Fig. 5 .

 65 Fig. 6. Vertical store system.

See http://moodle.org/

See http://drupal.org/

See http://scenari-platform.org/

See http://moodle.org


D Motta

SEARCH FOR THE θ 13 NEUTRINO MIXING ANGLE USING REACTOR ANTI-NEUTRINOS

The measurement of the last undetermined neutrino mixing angle θ 13 is the main goal of the future experimental research on neutrino oscillations. At present, θ 13 is only known to be much smaller than the two other mixing angles, θ 12 and θ 23 . The present bound, which is dominated by the result of the CHOOZ reactor experiment, is sin 2 2θ 13 < 0.08 (at 90 % confidence level). However, it is widely recognized that the potential of reactor antineutrino disappearance experiments has not been fully exploited yet. A rich experimental program is underway, which aims at exploring in the near future up to sin 2 2θ 13 0.01. The targeted sensitivity requires a clear-cut strategy to reduce significantly both statistical and systematical errors with respect to past reactor experiments. A key feature for the success of all projects is the installation of one or more near identical detectors. The experimental concept and the status of the upcoming or proposed reactor experiments, and as well the prospects of the reactor-based search for θ 13 are reviewed.

Introduction

In the last decade our understanding of the fundamental properties of neutrinos has made formidable progress. A variety of experiments measuring solar, atmospheric, reactor and accelerator neutrinos have indisputably shown that neutrinos produced in a specific flavor eigenstate oscillate to other flavors. This is proof that neutrinos have mass and that the weak interaction mixes the mass eigenstates. Experiments with sensitivities spanning several orders of magnitudes in the oscillation parameter space have pinned down the values of two mass-squared differences and two mixing angles (θ 12 and θ 23 ). For a review of the current status of the field, see [1]. On the third mixing angle θ 13 , experiments have only set an upper bound. This parameter translates the admixtures of ν 3 in ν e and hence drives the amplitude of the still unobserved ν x → ν y oscillations (where x or y is e) at the oscillation length determined by ∆m 2 13 . At present, θ 13 is only known to be much smaller than the two other mixing angles. The negative result of the Chooz reactor experiment [2] has provided the most stringent upper-bound 1 : sin 2 2θ 13 0.12 -0.20 (90 % C.L.) (1) where the interval corresponds to the "atmospheric" mass splitting allowed by the combined results of Super-Kamiokande, K2K and the first results of MINOS: ∆m 2 13 = 2.1 -3.0 • 10 -3 eV 2 (2σ interval) [2,4]. A global 1-degree-of-freedom analysis including all available oscillation data, where ∆m 2 13 and all other oscillation parameters are marginalized away, gives [4]: sin 2 2θ 13 < 0.08 (90 % C.L.)

1 A less stringent bound has been given by the other ∼ 1 km baseline reactor experiment, Palo Verde [3] The θ 13 mixing angle -of fundamental theoretical interest per se -turns out to be the key parameter to access experimentally CP violation in the leptonic sector, which in turn could explain the matter/anti-matter asymmetry in the universe. Several upcoming experiments aim at exploring with two different techniques and complementary approaches a large part of the region currently allowed by CHOOZ. Superbeam experiments will exploit the appearance channel ν µ → ν e by using 1 GeV neutrinos and long baselines (102 -10 3 km). The next generation of reactor experiments aim at measuring the disappearance of ν e of a few MeV on a ∼ 1 -2 km baseline, with improved sensitivity with respect to CHOOZ. These two experimental approaches are nicely complementary [5], as illustrated below.

In the ν µ → ν e search, the contribution of θ 13 to the appearance signal is entangled with unknown parameters, i.e. the CP-violating phase and the mass hierarchy (the sign of ∆m 2 13 ). As a consequence, the sensitivity to θ 13 is limited by the correlations between these parameters in the appearance probability, and by the degeneracies of the solutions. This would notably degrade the sensitivity in case no oscillation is observed. On the other hand, for a favorable combination of the nuance parameters, superbeam experiments have a chance to observe a signal even for sin 2 2θ 13 < 0.01 [6], which is out of reach for the upcoming reactor experiments.

Reactor experiments are sensitive to the disappearance 1 -P (ν e → ν e ), where P (ν e → ν e ) is the ν e survival probability after oscillation. It is shown [6] that to a very good approximation the sought signal is given by:

1 -P (ν e → ν e ) ≃ sin 2 2θ 13 sin 2 ∆m 2 13 L 4E ( 3
)
where L is the oscillation baseline and E the anti-neutrino energy. Eq. 3 shows that for reactor experiments the only unknown parameter controlling the signal is θ 13 . This means that for a source-detector distance tuned to have the oscillatory term near the maximum (L ∼ 1 -2 km), the disappearance provides the most clean measurement of θ 13 . For a small mixing angle, however, the disappearance of a tiny fraction of anti-neutrinos can be easily hidden by statistical fluctuations and especially by the systematic uncertainties on the number of expected events without oscillation. The reduction of the latter systematics is therefore the crucial challenge of all future reactor experiments.

2 Reactor-based search for θ 13

2.1 The past benchmark: CHOOZ CHOOZ [2] has been the last of a long series of short-to-middle baseline oscillation experiments run in the vicinity of a reactor power plant. The Chooz power station is located in the Ardennes region, northeast of France, very close to the Belgian border. The power plant consists of the two most powerful pressurized water reactors in the world, capable of yielding up to a total 8.5 GW of thermal power 2 . A single detector with a ∼ 5 t liquid scintillator target was located at an average distance from the two cores of 1.05 km. The experiment was stopped after a few months of data-taking, resulting in a total statistics of ∼ 2700 ν events. The measurement was fully consistent with the expectations for the case of no oscillation (Fig. 1). The result can be summarized by the integrated rate normalized to the MC prediction:

R = 1.01 ± 2.8 % (stat) ± 2.7 % (sys) (4)
In addition, Fig. 1 shows no evidence of a spectral deformation, which would be another strong signature of the oscillation (see Eq. 3). This result translates to the limit of Eq. 1 when combined with the present knowledge of ∆m 2 13 , and provides the dominant information for the best limit obtained with a global 2-d.o.f. fit, Eq. 2.

The strategy for the next generation reactor experiments

The sensitivity achieved by CHOOZ on a possible suppression of the integrated ν e flux is of the order of ∼ 5 %, as shown by Eq. 4. In the past years a large consensus has grown on the idea that the potential of a reactor-based search for θ 13 is far from fully exploited [7,8]. The present limit suffers from the large statistical and systematical uncertainties in CHOOZ, which was an experiment conceived for probing large mixing.

First of all, the next generation experiments need to improve statistics. This requires an optimal combination of high reactor thermal power, large target mass, good efficiency and long run-time. Though trivial in principle, the latter requirements are not "painless": projects envisaging very large target masses are expensive; a long data-taking (∼ 3 y) demands an extremely stable and well monitored detector.

However, most challenging is the reduction of the systematic uncertainties: while it seems realistic to reduce the statistical error to < 0.5 %, the control of systematics to this level is not possible with an approach "à la CHOOZ". Most importantly, because the ultimate knowledge of the neutrino source (the reactors) is limited to ∼ 2 %. Secondly, because several detector parameters influencing the neutrino-rate can be determined with a poor accuracy even after calibrations, like the number of target protons, efficiency of the analysis cuts, capture on Gd versus H, ν-produced neutrons spilling outside or inside the target volume, etc. A more detailed discussion about these and other systematics follows in Sec. 2.5.

For the above reasons, all of the next generation projects propose to deploy one or several near detectors, strictly identical to the detector that searches for the oscillation. These near detectors will monitor the nearly-unoscillated flux and spectrum with "identical" response to that of the far detector. Most of the reactor-related uncertainties are correlated between detectors and hence perfectly cancel, and many the detector systematics are reduced as well, provided that the response of near and far detectors are identical (after calibration). An additional, challenging option proposed, for example, for the Daya Bay experiment, consists in building several movable detectors, which would be periodically swapped between near and far sites. Provided that this operation can be done reliably and without affecting the detector response, some of the remaining systematics would cancel in this way. The sensitivity of the next generation experiments will ultimately depend on the accuracy within which two detectors can be made identical, or to which extent their relative response can be known.

Experimental concept

As in past reactor experiments, ν e are detected via the reaction:

ν e + p → e + + n (5)
Eq. 5 has a threshold of 1.8 MeV. The neutron carries a very little kinetic energy, hence the positron energy deposition, boosted by the 1.024 MeV from its annihilation, measures the ν e energy. An H-rich organic liquid scintillator is used as target and detection medium. The neutron issued from the interaction thermalizes in the scintillator and is then captured. The resulting delayed gamma emission provides a powerful signature to reject the background.

In order to enhance the background rejection, Gd is dissolved in the scintillator at a concentration of ∼ 1 g/l. Captures on 155 Gd and 157 Gd, which have huge cross sections for thermal neutrons, are followed by the emission of a ∼ 8 MeV gamma cascade, well beyond the background due to the natural radioactivity. A typical ν e event is then given by the coincidence of the prompt e + and a delayed ∼ 8 MeV energy deposition, within a time window of ∼ 100 µs (the characteristic n capture time for ∼ 1 g/l Gd concentration is ∼ 30 µs).

A non vanishing value of θ 13 would show up in the data as an energy-dependent suppression of the rate in the far detector with respect to the near detector. It can be convenient to separate the experimental information giving sensitivity to θ 13 in two contributions: an integrated spectrumaveraged suppression and a spectral deformation. It will be later shown that the relative weight of the two can vary, depending on the characteristics of the experiment. As an example, Fig. 2 shows the kind of oscillation signal that would be seen in Double Chooz [10] (described in Sec. 3.1) for a relatively large θ 13 (sin 2 2θ 13 = 0.1) and ∆m 13 = 2.5 • 10 -3 eV 2 .

The detector design is an evolution of CHOOZ. Owing to what has been learned in the past, the main improvements concern the minimization of the information losses, (dead volumes, non-fully-contained events, badly reconstructed events) and the reduction of the backgrounds (overburden, µ-veto-systems, shielding, material radio-purity). As an example, Fig. 3 illustrates the design of the Double Chooz detector (all other projects propose very similar concepts). From the center, the following sub-systems are typically found:

A central cylindrical transparent vessel containing several tons of liquid scintillator doped

with Gd at ∼ 1 g/l. This is the target for the ν e interactions.

2. A volume concentric with the target and containing undoped scintillator. This region, called "γ-catcher", acts as a calorimeter: its purpose is to assure the full collection of the positron energy, and a good containment of the gamma cascade following n-capture on Gd.

3. A non-scintillating, highly transparent liquid buffer (mineral oil) contained in a stainless steel tank, on which low-background photomultiplier tubes (PMT) are mounted to detect the scintillation photons. The buffer volume is one of the main improvements with respect to CHOOZ. It attenuates the gammas emitted by the PMTs, and thus reduces the rate of accidentals.

4. An instrumented volume, called µ-veto, to tag and reject events induced by cosmic muons.

5. An external shielding to protect the innermost volumes from the gammas of the natural radioactivity in the surrounding rocks.

A system of concentric chimneys will allow the introduction of the filling tubes and nonpermanent calibration devices. Near and far detector both need to be deployed underground to insure adequate protection against the cosmic rays, which would produce an overwhelming background at surface. A discussion about backgrounds will follow in Sec. 2.6.

Sensitivity

In this section some general remarks about the sensitivity of the reactor-based search for θ 13 are presented. The discussion is based on the work published in [5] and earlier by the same group. The original plot is from [5]. Here the three regimes discussed in the text are highlighted with different levels of gray. For reference, the statistics of Double Chooz realistically achievable after 3 years of datataking is indicated.

The sensitivity, here defined as the largest value of sin 2 2θ 13 that would be consistent with data at a certain confidence level (typically 90 %) if no oscillation is observed, depends on both the statistical and systematical errors. The latter can be separated in three terms: an error completely correlated between near and far detector(s) (σ corr ), the relative global normalization uncertainty between the two detectors (σ norm ), and a bin-to-bin error non-correlated between energy bins and between detectors (σ bin-to-bin ). Fig. 4 shows the evolution of the sensitivity with the total statistics in the far detector. With all systematic effects switched off, the sensitivity would follow a trivial ∝ √ N statistical law. An experiment of the size of Double Chooz (8.2 t) would then be sensitive to sin 2 2θ 13 ∼ 0.015. When the systematics are turned on, three "regimes" appear: a "low statistics regime", where the sensitivity is dominated by the rate ratio and is statistics-limited; an "intermediate statistics regime", where both rate ratio and spectral deformation contribute to the sensitivity, the former limited by σ norm , the latter by the statistics per energy bin; and a "high statistics regime", where most of the sensitivity comes from the spectral deformation and is hence limited by σ bin-to-bin . The transition between these regimes depends on the size of the systematics. However, several general conclusions can be drawn.

The sensitivity on any experiment will first rapidly roll along the curve of Fig. 4 (note the log-log scale), and will eventually reach a plateau, which is ultimately set by statistics or systematics. For a small, "first generation" detector (∼ 10 t), the "roll-down" will stop at the second regime. In this case the final sensitivity critically depends on σ norm and the major experimental efforts need to be dedicated to the reduction of this systematics. Fig. 4 shows that, for a constant σ norm , a significant improvement of the sensitivity requires fully entering the third regime, corresponding to a 200 t target mass. A "second generation" experiment of intermediate size (∼ 50 -100 t) will therefore need also an improvement of σ norm to outperform a smaller detector. A very large detector, instead, could afford ignoring σ norm and simply rely Table 1: Summary of the systematic uncertainties. The "realistic now" column is based on assessments in the Double Chooz proposal [10] and on the "baseline" uncertainties estimated in Daya Bay [11]. The "wished for the future" column reflects the Daya Bay R&D goals (without detector swapping). on the analysis of the spectral distortion. In this case the systematic to keep under control is σ bin-to-bin , hence any possible energy-dependent difference between the signals at the near and far sites (e.g. due to the background subtraction at different sites, or biased analysis cuts).

CHOOZ

Systematics

The previous section has outlined how critically the systematical errors affect the ultimate sensitivity of the reactor-based search for θ 13 . Table 1 presents a summary of all identified systematics, as they were measured in CHOOZ, as they are estimated after the R&D carried out in Double Chooz, and as they are foreseen in Daya Bay (Sec. 3.2). The use of identical near detectors suppresses the source-related systematics from 2.1 % to 0.1 %. This remaining 0.1 % comes from the error of the reactors-to-near detector(s) distance, including the the finite size of reactors and detectors, and the fluctuations of the ν e production barycenter. Experiments installed in a multi-core (¿ 2) site, like Daya Bay and RENO (Sec. 3.3), suffer also from the additional uncertainty due to the different repartition of the signal from each single reactor in the near detector(s), compared to the far detector.

As regards the number of target protons, in CHOOZ this error was quite large (0.8 %). An important improvement is guaranteed by the use of the same batch of liquid scintillator in the near and far detectors. This assures that the uncertainty due to the chemical composition (H mass fraction) is canceled out. The only contribution left comes from the relative error between mass or volume measurements. Furthermore, the use of identical near and far detectors cancels the "spill-in, spill-out" error (imperfect compensation between ν-events in the γ-catcher with a n spilling into the target volume, and ν-events in the target with the n spilling out to the γ-catcher), which is equivalent to an additional uncertainty on the target proton number.

A strong reduction is also envisaged for the detector-related systematics, i.e. the detection efficiency. In this case as well, the multi-detector concept transforms uncertain absolute efficiency determinations to better constrained relative comparison. The use of the same scintillator and calibration devices in detectors of identical geometry will be crucial. Moreover, a significant reduction is assured by the improvement of the detector design, which will allow to limit the number of analysis cuts. In fact, several "quality" cuts were needed in CHOOZ to suppress the accidentals (Sec. 2.6). A simplified event selection will be possible thanks to a reduction of the rate of singles (better shielding) and with an improved containment of the e + events and Gd cascades.

In conclusion, the reduction of the total systematic error from 2.7 % to < 0.6 % (the Double Chooz conservative goal) seems realistic nowadays. The goal of < 0.2 %, required for a significant boost of the sensitivity in next-generation projects, is not yet validated by a specific R&D. At this regard, the upcoming first generation experiments will set important benchmarks and pave the ground for a possible further step in sensitivity.

Backgrounds

Background subtraction is another source of systematics, as it can both affect the global normalization between near and far detectors and as well the spectral shape. CHOOZ had the chance to measure the background in situ with both reactors off, as it started data-taking just before the power plant commissioning. The chance for a "signal-free" background measurement depends on the characteristics of the site. In a 2-core plant, like in Double Chooz, a long simultaneous stop of both reactors is extremely rare, but not impossible on a 3y time scale. For a multi-core site, this event is nearly impossible and one can simply expect a modulation of the total power 3 .

Three different classes of background can be identified: accidentals, due to the random coincidence of an environmental gamma and a n-like event within the ν time window; fast neutrons, which can produce proton-recoils in the target (misidentified as e + ), and then be captured; and long-lived cosmogenic radio-isotopes with significant branching for β -decay followed by neutron emission ( 9 Li, 8 He).

Accidentals can be measured in situ even with reactors on, through the rates and spectra of the singles. In order to reduce the subtraction error, the rate of gammas penetrating the target must be reduced with an accurate material selection (low radioactive contaminants) and an adequate shielding.

Fast neutrons produce a background with very similar signature as the ν-events, however with a nearly flat spectrum. Hence the rate can be extrapolated from the part of the spectrum at higher energy than the signal. Since fast neutrons are secondary particles produced by cosmic muons, a large and effective µ-veto is very helpful.

Stopping and showering µ can originate spallation reactions on 12 C that produce 9 Li and 8 He. These isotopes can decay with a β-n sequence that is indistinguishable from a ν event. Lifetimes are of the order of ∼ 10 2 ms, which makes it hard to impose a long enough veto to all crossing muons. A possibility would be developing a more sophisticated µ detector, capable of tagging muons as "showering" or "stopping". A longer veto could be applied to these events only, without increasing the dead-time too much.

Generally speaking, all the discussed backgrounds rapidly attenuate with the laboratory overburden. A compromise must be reached between costs and allowed background rate, bearing in mind that the better the knowledge of the background, the higher the rate that can be tolerated. As a rule of thumb, a first generation experiment needs to keep the background uncertainty per energy bin below ∼ 1 % of the signal. A second generation experiment should

Upcoming and proposed experiments

By the time these proceedings are written, only three experiments have survived the decisions of their funding agencies: Double Chooz, Daya Bay and RENO. After Braidwood was turned down by the negative decision of the DOE in April 2006, another project, the Japanese KASKA, has been stopped upon rejection of its funding requests. The KASKA collaboration has since then joined Double Chooz.

Double Chooz

Double Chooz [9,10] is the most advanced among these projects. The R&D is essentially completed, the detector design finalized and the project is now fully entering the construction phase. Double Chooz will be installed nearby the same nuclear power plant of the CHOOZ experiment. Table 2 shows a synopsis of the experiment. One of the main advantages of Double Chooz is the availability of the CHOOZ laboratory for the integration of the far detector, which allows the experiment to be faster and cheaper than any other project. As regards the near site, the present baseline in the discussions with EDF assumes the construction of a laboratory at ∼ 280 m from the cores, with an overburden of ∼ 80 mwe.

The Double Chooz collaboration is composed of several institutions from Europe (France, Germany, England, Russia and Spain), US and Japan. The project is fully approved and funded in Europe, awaits approval in US and Japan in 2007. A close collaboration with the power plant management has been established, both for the refurbishing and recommissioning of the far laboratory, and the construction of the near laboratory. The current planning considers material procurement to be completed by 2007, with the start of the integration of the far detector in the fall of 2007 and commissioning at the beginning of 2008. Parallel to this phase, the construction of the near laboratory will proceed. The site is expected to be available for detector integration in 2009. Double Chooz will then be operative with both detectors by 2010.

The intense R&D work carried out by the Double Chooz collaboration has validated the robustness of the detector concept and the feasibility of the sensitivity goals. In the first phase with the far detector alone, Double Chooz will attain a sensitivity to sin 2 2θ 13 of 0.06, and then will reach its goal sensitivity of better than 0.03 by 2013. Table 3: Daya Bay synopsis. Statistics given with the 3 sites and 6 reactors on, for near and far sites, 2/4 for the mid site (11.6 GW th ). Note that a similar sensitivity as Double Chooz is claimed for the Mid detector, even though the contribution of the Ling Ao I reactors, which will account for more than half of the signal, will not be monitored by the corresponding near detector.

Reactors Detectors

(M = 20 t) In order to achieve the desired sensitivity, Daya Bay relies on a number of ambitious design options and merit factors of its site. The mass scale of the experiment will be of one order of magnitude higher than Double Chooz, with a total target mass of 160 t of Gd-loaded scintillator distributed among one far and two near sites. The Daya Bay site has ideal geology, with nearby mountains for very deep underground laboratories. The experiment will benefit from an intense total ν e flux, however at the cost of a non trivial reactor arrangement. Four reactors are distributed on 2 separate sites 1 km away from each other, and a third 2-reactor site will be added by 2011.

Sites

The proposed design envisions splitting the total target mass in 8 × 20 t modules (each weighting ∼ 90 t with γ-catcher and buffer) placed side-by-side in big water pools measuring 16×16×10 m 3 , used as both shielding and water-Cherenkov veto; 4 modules in the far laboratory, and 2 in each of the two near detectors. An option the collaboration bets on is the possibility to make all modules movable on rails between experimental sites. The synopsis of the Daya Bay project is shown in Table 3.

Daya Bay is now at a R&D and preparation stage. This phase will have to finalize the detector design and answer several questions concerning a realistic estimation of the systematical errors and the feasibility of the swapping option. For its ambitious goals and mass scale, Daya Bay should be considered a "second generation" project. The collaboration, however, is trying hard to be competitive as a first generation experiment as well, by deploying two additional modules at an intermediate site at ∼ 1 km distance with only one near site on. The mid experiment is supposed to be running while the rest of the civil works and installations are under completion. Whether this "fast Daya Bay" option is worth the effort will be answered by ongoing studies.

RENO

RENO [12] is a 20 t scale experiment proposed by a South-Korean collaboration, to be installed at the Yonggwang nuclear power plant. The site has favorable geology with nearby hills potentially providing adequate overburden for near and far detector. The total ν e flux is 3 times higher than in Chooz, however this flux is produced by 6 reactors lined up over 1.5 km. As a consequence, a large part of the reactor-related systematics cannot be canceled by the near detector (see Table 4). The RENO collaboration proposes therefore to install smaller "very near" detectors to monitor the neutrino flux of each reactor. The claimed sensitivity would then be ∼ 0.02.

RENO is still at a very early stage of the R&D, however the Korean funding agencies seem very supportive.

Ideas for the future

Recalling the discussion in Sec. 2.4, the sensitivity of the upcoming experiments Double Chooz and Daya Bay will be ultimately limited by the error of the relative normalization between near and far detector(s). Decreasing this systematics further with respect to the very challenging goal of σ rel < 0.2% does not seem realistic for the moment. To push the sensitivity beyond the barrier of sin 2 2θ 13 < 0.01 it is necessary to increase the statistics so much to enter the third regime of Fig. 4, where the spectral shape distortions dominate the sensitivity. Provided that the measurement of the ν e spectra can be made with the required accuracy and that after Double Chooz, Daya Bay and the superbeam experiments the question about θ 13 is still of the highest scientific priority, the only viable strategy seems installing a 200 t detector at the optimal distance from a reactor site, shielded by a very good overburden. The near detector is no longer required to be identical, provided that no energy-dependent biases are introduced in this way. The ANGRA [13] project aims at exploring this solution during the forthcoming years. The detector would be installed by the nuclear power plant of Angra dos Reis, in Brazil. At present the project is in the phase of conceptual design and sensitivity study.

Conclusions and prospects

Double Chooz is fully approved in Europe and has now entered the material procurement phase. It will be the first experiment to improve the present limit on sin 2 2θ 13 and will reach its goal sensitivity by 2013, roughly when T2K is expected to start releasing competitive results on θ 13 . Daya Bay, supported by a very strong US-Chinese commitment, aims at surpassing the Double Chooz sensitivity with a bigger and more precise experiment. The realistically attainable systematics, the related sensitivity, the feasibility of the proposed challenging solutions, and the cost and schedule of the project are currently under investigation.

Most theoretical models are unable to explain why θ 13 should be much smaller than the two other mixing angles. If the small (1,3) ν-mixing is merely accidental, θ 13 is most likely not very far from the present bound and will be measured within few years from now by Double Chooz, and then confirmed with higher significance by Daya Bay. Early indications about the CP phase and mass hierarchy could then be deduced through the comparison with the results of the superbeams.

A much smaller (1,3) mixing would then be a strong hint for a new discrete flavor symmetry. The synergies between superbeams and second generation reactor experiments (class sin 2 2θ 13 < 0.01) will then be very powerful towards the interpretation of the nature.

Figure 1 :

 1 Figure 1: Left: Signal measured in CHOOZ [2], after background subtraction (markers with error bars), superimposed on the expected spectrum for the case of no oscillation. Right: ratio of data to predictions.

Figure 2 :

 2 Figure 2: Far/Near rate ratio in Double Chooz after 3y data-taking, for sin 2 2θ 13 = 0.1 , ∆m 2 13 = 2.5 • 10 -3 eV 2 . Error bars are statistical.

Figure 3 :

 3 Figure 3: Sketch of the Double Chooz detector. Other projects propose very similar designs.

Figure 4 :

 4 Figure 4: Sensitivity of a reactor experiment to sin 2 2θ 13 , as a function of the total cumulative statistics.

Table 2 :

 2 Double Chooz synopsis. The sensitivity in the first row refers to the first experimental phase, in which the far detector will run alone.

	Reactors	Detectors (M = 8.2 t)	Sites	Civil works	Stat (d -1 ) (incl. ǫ)	B/S (%)	Start	Sensitivity (sin 2 2θ 13 )
	1 site 2 cores 8.5 GW th	1 Far 1 Near	D = 1.05 km (300 mwe) D ∼ 280 m (∼ 80 mwe)	refurbishing ∼ 40 m shaft + lab	50 550	1 0.5 2010 2008	0.06 (1.5 y) < 0.03 (3 y)
	make roughly two times better.

  Daya Bay is a project aiming at a final sensitivity of ∼ 0.01. It is based on a strong US-Chinese collaboration that also includes Russian, Taiwanese and Czech institutions. It is approved in China and strongly supported by the DOE in US. The experiment will be installed nearby the Daya Bay / Ling Ao nuclear power plants, about 55 km away from Hong Kong.

				Civil works	Stat (d -1 )	B/S (%)	Start	Sensitivity (sin 2 2θ 13 )
	3 sites 6 cores	4 Far (1 site)	D ∼ 1.8 km (∼ 1000 mwe)	∼ 1 km tunnel + 1 cavity	200	0.5 2010	0.01 (3 y)
		2 Mid (1 site)	D ∼ 1 km (∼ 550 mwe)	∼ 1 km tunnel + 1 cavity	200		2009	0.035 (1 y)
	17.4 GW th	4 Near (2 sites)	D ∼ 400 m (∼ 300 mwe)	cavities ∼ 1 km tunnels + 2	2000	0.5
	3.2 Daya Bay

Table 4 :

 4 Relative contribution (%) of the 6 reactors in the proposed RENO experiment.

		R1	R2	R3	R4	R5	R6
	Near	3.0	7.8 39.2 39.2 7.8	3.0
	Far	14.8 16.9 18.3 18.3 16.9 14.8

The experiment took data during the start-up phase of the nuclear plant, hence a reduced power was available.

It must be noted that single reactors are periodically off once a year during ∼2 weeks for refueling.