20/11/2020
Logo of Marie Skłodowska-Curie Actions

PhD position 05 – MSCA COFUND, AI4theSciences (PSL, France) - “Dark energy studies with the Vera Rubin Observatory LSST & Euclid - Developing a combined cosmic shear analysis with Bayesian neural networks”

This job offer has expired


  • ORGANISATION/COMPANY
    Université PSL
  • RESEARCH FIELD
    AstronomyCosmology
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    26/02/2021 23:00 - Europe/Brussels
  • LOCATION
    France › Paris
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/09/2021
  • EU RESEARCH FRAMEWORK PROGRAMME
    H2020 / Marie Skłodowska-Curie Actions COFUND
  • REFERENCE NUMBER
    AI4theSciences-PhD-05
  • MARIE CURIE GRANT AGREEMENT NUMBER
    945304

OFFER DESCRIPTION

“Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.

26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2020 call will offer up to 15 PhD positions on 24 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.

 

Description of the PhD subject: “Dark energy studies with the Vera Rubin Observatory LSST & Euclid - Developing a combined cosmic shear analysis with Bayesian neural networks”

 

Context - Motivation

During the last decade, cosmology has entered a precision era, leading to the prevalence of the standard cosmological model, ΛCDM. Nevertheless, the main ingredient of this model, dark energy, remains mysterious while dominating the energy budget of the Universe. Its comprehension is the current Graal of this domain. The next generation of cosmological surveys, among which Legacy Survey of Space and Time (LSST) of the Vera Rubin Observatory (on the ground) & Euclid (in space),both starting in 2022, are in that regard the most important projects for the next 10 years.

These surveys, when combined, will map thousands of square degrees of sky in a multiwavelength manner with sub-arcsec resolution. This will result in the detection of several tens of billions of sources, enabling a wide range of astrophysical investigations and providing unprecedented constraints on the nature of dark energy and dark matter. The scope of the PhD topic is at the crossing of the two surveys. More precisely, the PhD topic discussed here is focused on developing analyses for weak gravitational lensing combining the data of LSST and Euclid.

The gravitational lensing corresponds to the deflection of light from distant sources (background galaxies) due to the bending of space-time by matter along the line of sight, resulting in distortions and displacements of their image. The statistical study of weak gravitational lensing distortions at large scales provides a “mapping” of the matter (dark or visible) between the observer and source (more accurately, the effect of coherent deformation described here is called cosmic shear). This type of measurement gives a window on the properties and the evolution of cosmic structures as well as the geometry of the Universe. Its study can therefore bring higher constraints on the origin of the current accelerated expansion of the Universe that led to the notion of dark energy. In the absence of systematic errors, weak lensing is even recognised as the single most constraining probe of dark energy. As such, it is one of the main science drivers for both LSST and Euclid.

However with an increased sensitivity compared to previous surveys, LSST and Euclid will bring their share of new challenges to allow the proper use of weak gravitational lensing. First of all, the sheer volume of produced data requires the development of new types of analyses that allow a more efficient processing (the Rubin Observatory will survey the visible night sky every night for ten years, building a 500 PB database of images). Plus novel complexities arise and have to be dealt with to ensure the full scientific return of these projects. To address these challenges, our group has started to develop an approach based on Bayesian neural networks (BNNs,[1]). The BNNs offer a formalism to quantify and propagate uncertainties associated with deep neural networks models and also with the data themselves, which are both key for cosmological analyses.

To give an example, as more and more galaxies and stars populate the images, the local density increases and the projected objects naturally overlap. This phenomenon, referred to as blending, impedes the ability to measure properly the shapes of individual objects and will affect more than 60% of the galaxies in LSST. To address this issue, deep learning brings a possible solution, with an efficient use of the joint multi-band processing of LSST and Euclid images. The images are fed to a BNN to separate overlapping galaxies before measuring their shape and this approach brings an improvement to the use of one of the surveys alone (as it brings the complexity but also the power of a multi-resolution and multiwavelength approach to the problem). We have demonstrated the feasibility of this approach first in a simple context for Euclid [2], and then in a configuration with several simulated galaxies per image in a recent publication for LSST and Euclid [3]. This work is still part of an ongoing effort in the LSST Dark Energy Science collaboration, referred to as DESC.

 

Main references:

[1] Tom Charnock, Laurence Perreault-Levasseur, François Lanusse, Bayesian NeuralNetworks, arXiv:2006.01490

[2] Boucaud et al., Photometry of high-redshift blended galaxies using deep learning,Monthly Notices of the Royal Astronomical Society, staa3056, arXiv:1905.01324

[3] Bastien Arcelin, Cyrille Doux, Eric Aubourg, Cécile Roucelle, Deblending galaxies withVariational Autoencoders: a joint multi-band, multi-instrument approach, Monthly Noticesof the Royal Astronomical Society, staa3062, arXiv:2005.12039v1

[4] Bayesian Deep Learning for Cosmology and Gravitational Waves workshop, APC Laboratory, France, 3-5 March 2020,

 

Scientific Objectives, Methodology & Expected results

Going beyond this first task of detecting and separating individual galaxies, the successful candidate would work on a direct statistical measurement of the average shape of blended objects with BNNs, leading to a local estimate of the cosmic shear —a well-defined objective, achievable in three years considering the current state-of-the-art. A success metric is the amount of systematic errors (after calibration) in the shear estimate, as measured by existing challenges.

This PhD will occur in the scope of AstroDeep, a 4.5-year ANR project started in October 2019 at APC. This project intends to build a collaboration between cosmologists, statisticians and computer scientists, focused on the quantification of uncertainties in cosmological analyses with Bayesian neural networks. Probabilistic machine learning has recently attracted a lot of attention, both from the AI community and the physics community.

The APC Laboratory hosting the future PhD candidate tends to build a two-pronged approach with these analyses in the objective to answer the challenges of this decade observational cosmology, but also to build strong expertise in probabilistic machine learning that will with no doubt lead to major breakthroughs. This effort has already been started within the AstroDeep project by contacting the main developers of TensorFlow Probability (Google framework for designing and training BNNs on GPUs) end of 2019 to start a collaboration, and inviting them to our first workshop of a series on Bayesian deep learning in March 2020 [4] (the second edition, which will be held in 2021, is already partly funded by LSST DESC).

The student joining the project would beneficiate greatly from the direct collaboration with Junpeng Lao, one of the main developers of the Tensorflow Probability framework at Google, and expert on the application of Bayesian statistics to deep neural networks. His background in Physics will be an asset in the student's mentoring. Together, we will build knowledge in this emergent field, working on the comprehension, characterization and use of the BNNs, and also apply these techniques to astrophysical images to perform a multi-instrument and multi-wavelength joint analysis of galaxy fields. If successful, the implications of this work could drastically reduce the bias on cosmic shear measurements and release an essential tool for observational cosmology to the community. Not to mention that LSST and Euclid data will become available for science in 2022, during the PhD thesis (LSST is starting its commissioning in 2021), making these studies all the more interesting as the scientific environment will be extremely dynamic and competitive.

 

International mobility

As members of the LSST DESC, our group (and therefore the PhD student) is involved in a very broad international collaboration gathering more than 20 countries. The student will have interactions within this collaboration on a daily basis. International mobility itself is currently quite difficult to anticipate but if at all possible will be expected to occur for collaboration meetings, conferences and sprint weeks. A longer mobility could be thought of and would be very beneficial to the PhD student (even if hypothetical at the moment considering the pandemics), especially with the LSST DESC group at Stanford University, also very involved on the galaxies deblending challenge, or with the LSST group at Princeton University, the leaders on data processing for LSST.

Our group also has many contacts in Europe through the scientific collaborations on weak lensing measurements especially at the Oscar Klein Center (Stockholm, Sweden) or at the German Center for cosmological lensing (Bochum). A mobility in these groups could strengthen our bonds as well as benefit the PhD student.

Finally, several short visits to Google CH (Zürich, Switzerland) are to be expected to work more closely with Junpeng Lao, the co-supervisor specialised in both Bayesian statistics and its implementations in TensorFlow Probability.

 

Thesis supervision

Eric Aubourg and Junpeng Lao

 

PSL

Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 36th in the 2020 Shanghai ranking (ARWU).

More Information

Benefits

  • Opportunity to conduct academic research in a top 100 university in the world.
  • High-quality doctoral training rewarded by a PhD degree, prepared within Observatoire de Paris - PSL and delivered by PSL.
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 3100 € gross employer (including employer tax) per month or approximately a 2228 € gross salary per month.
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
  • Short stay(s) or secondment in France or abroad are expected.
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.

Eligibility criteria

  • Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call.
  • There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule).
  • Applicants must declare to be available to start the programme on schedule.

For submitting your online application, go to: https://www.psl.eu/recherche/grands-projets-de-recherche/projets-europee...

 

The online application should contain the following documents:

  • English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
  • International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
  • Two academic reference letters.
  • A statement duly signed on the mobility rules, availability, and conflicts of interest.

The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.

Selection process

The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (March-April 2021), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (June 2021) to further assess their qualifications and skills according to the predefined selection criteria.

All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.

 

The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.

Additional comments

The AstroParticle and Cosmology Laboratory (APC) is a Joint Research Unit (UMR) created in 2005. The APC brings together about 75 permanent researchers and about 50engineers, technicians and administrative staff. Including non-permanent staff (doctoral students, post doctoral fellows, foreign visitors), some 200 people make up this structure, led in addition to the Université de Paris (UP), by the CNRS (represented by three of its Institutes: mainly IN2P3, but also INSU and INP), the CEA (DSM/IRFU) and the Observatoire de Paris - PSL, and the CNES.

 

The activity of the laboratory is focused on four main themes: Cosmology, High-Energy / Astrophysics, Neutrinos, Gravitation. In addition to the experimental or observational activities described above, the topics covered by the laboratory are studied by the Theory group. The laboratory also has an extensive program of interdisciplinary collaborations, notably with geosciences in the context of the Laboratory of Excellence UnivEarthS​. APC maintains close ties with the Paris Centre for Physical Cosmology (​PCCP​) chaired by George Smoot, winner of the 2006 Nobel Prize, which develops research, training and knowledge dissemination activities in cosmology.

 

Observatoire de Paris - PSL is a leader institution in Astronomy and Space Sciences in France. Observatoire de Paris - PSL fulfils three principal missions: research, teaching and public outreach. The research carried out at Observatoire de Paris - PSL covers all the fields of contemporary Astronomy and Astrophysics. The Observatory develops and runs national and international services and puts its expertise at the service of major projects for observing the Universe. It contributes thereby to the creation of large instruments (both ground-based and space-borne), participates in major surveys, large simulations, in the Virtual Observatory, and is also involved in laboratory studies.

 

One third of all the scientists in France working in Astronomy work at Observatoire de Paris - PSL. All fields of Astronomy & Astrophysics are covered, as well as some aspects of fundamental physics and the history of science and Astronomy. Paris Observatory-PSL hosts six departments (GEPI, LESIA, LERMA, LUTH, SYRTE, LERMA), one scientific Unit (Nançay Radio Astronomy Station) and one Institute (IMCCE), which provides and publishes ephemerides of solar system bodies. All of these entities are Joint Research Units with CNRS and some are associated with other Paris Universities. They are spread over three locations : Paris, Meudon and Nançay (Research Center in Radioastronomy).

Web site for additional job details

Required Research Experiences

  • RESEARCH FIELD
    Astronomy
  • YEARS OF RESEARCH EXPERIENCE
    1 - 4

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Astronomy: Master Degree or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

  • Master 2 in Astrophysics and/or Computer Science.
  • Fluent in Python.
  • Knowledge of Bayesian statistics and machine learning would be a good advantage.

Work location(s)
1 position(s) available at
AstroParticule et Cosmologie, Observatoire de Paris - PSL
France
Paris
75013
10, rue Alice Domon et Léonie Duquet

EURAXESS offer ID: 579302

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