4 sujets /DAp/LCS

Dernière mise à jour : 11-08-2020


 

Machine Learning for Euclid Mass Mapping and Cosmological Parameter Estimation

SL-DRF-20-0313

Research field : Artificial intelligence & Data intelligence
Location :

Direction d’Astrophysique (DAP)

Laboratoire CosmoStat (LCS)

Saclay

Contact :

Jean-Luc STARCK

Starting date : 01-10-2020

Contact :

Jean-Luc STARCK
CEA - DSM/IRFU/SAp/LCS

01 69 08 57 64

Thesis supervisor :

Jean-Luc STARCK
CEA - DSM/IRFU/SAp/LCS

01 69 08 57 64

Personal web page : http://jstarck.cosmostat.org

Laboratory link : http://www.cosmostat.org

The Euclid satellite, to be launched in 2022, will observe the sky in the optical and infrared, and will be able to map large scale structures and weak lensing distortions out to high redshifts. Weak gravitational lensing is thought to be one of the most promising tools of cosmology to constrain models. Weak lensing probes the evolution of dark-matter structures and can help distinguish between dark energy and models of modified gravity. Thanks to the shear measurements, we will be able to reconstruct a dark matter mass map of 15000 square degrees. Mass mapping entails the construction of two-dimensional maps using galaxy shape measurements, which represent the integrated total matter density along the line of sight. Small- field mass maps have been frequently used to study the structure and mass distribution of galaxy clusters, whereas wide-field maps have only more recently become possible given the broad observing strategies of surveys like CFHTLenS, HSC, DES, and KiDS. Mass maps contain significant non-Gaussian cosmological information and can be used to identify massive clusters as well as to cross-correlate the lensing signal with foreground structures.

A standard method to derive mass maps from weak-lensing observations is an inversion technique formulated by Kaiser & Squires [2]. It has many limitations, however, including the need to smooth the data before (and often after) inversion, thereby losing small-scale information. An alternative method called GLIMPSE has been developed in the CosmoStat laboratory based on sparse reconstruction that avoids this problem and improves the recovery of non-Gaussian features [3, 4]. The algorithm has been tested on simulations and was also recently used to study the A520 merging galaxy cluster with Hubble Space Telescope data [5]. More recently, machine learning has emerged as a promising technique for mass map recovery [6].

The goal of this thesis is to i) compare this technique to the state of the art and investigate if it can be used in practice, ii) extend the method for spherical data, and iii) develop a new machine learning approach to estimate the cosmological parameters. At the core of this new statistical framework will be the development of fast and differentiable cosmological simulations capable of emulating the Euclid survey under various cosmologies. This simulation tool will be based on the FastPM N-body simulation code [7] and implemented directly in the TensorFlow machine learning framework, yielding a differentiable physical forward simulation pipeline which can be directly interfaced with deep learning components or with inference techniques relying on having access to the derivatives of the simulation.

As part of the CosmoStat Laboratory, located at CEA Saclay, the successful candidate will be embedded in a leading French research group, heavily involved in the preparation of the Euclid space mission, and with a long tradition of developing cutting-edge statistical tools for the analysis of astronomical and cosmological data.

1. Bartelmann, M. & Schneider, P. 2001, Phys. Rep., 340, 291. ?

2. Kaiser, N. & Squires, G. 1993, ApJ, 404, 441. ?

3. Leonard, A., Lanusse, F., & Starck, J.-L. 2014, MNRAS, 440, 1281.

4. Lanusse, F., Starck, J.-L., Leonard, A., & Pires, S. 2016, A&A, 591, A2.

?5. Peel, A., Lanusse, F., & Starck, J.-L. 2017, ApJ, 847, 23.

6. Niall Jeffrey et al, submitted. https://arxiv.org/abs/1908.00543

7. Y. Feng, M. Yat Chu, U. Seljak, and P. McDonald. MNRAS, 463(3):2273–2286, 2016.
Variational Inference for Joint Estimation of Cosmic Shear, PSF, and Galaxy Morphologies

SL-DRF-20-1144

Research field : Artificial intelligence & Data intelligence
Location :

Direction d’Astrophysique (DAP)

Laboratoire CosmoStat (LCS)

Saclay

Contact :

Jean-Luc STARCK

Starting date : 01-10-2020

Contact :

Jean-Luc STARCK
CEA - DSM/IRFU/SAp/LCS

01 69 08 57 64

Thesis supervisor :

Jean-Luc STARCK
CEA - DSM/IRFU/SAp/LCS

01 69 08 57 64

Personal web page : http://jstarck.cosmostat.org

Laboratory link : http://www.cosmostat.org

The upcoming Euclid ESA space telescope will aim to shed some much needed light on the physical nature of dark energy and dark matter by imaging the sky with a very high resolution. One major hurdle in the Euclid cientific exploitation of the images is that an exquisite control of the telescope instrumental response is required in order to precisely measure the shape of distant galaxies. With current methods, this delicate image processing task is prone to biases and errors that may hinder the scientific goals of this space mission.



The goal of this PhD thesis is to develop a hierarchical probabilistic model of the observed Euclid images combining physical models with Deep Learning components accounting for unknowns factors. Thanks to this hybrid Physical/Deep Learning generative model of the observed images,

it will be possible for the first time to jointly infer from the data both a Bayesian posterior distribution of the physical parameters and the parameters of the deep learning models.
Cross-correlations between cosmological probes from Euclid, BOSS/e- BOSS, Planck and beyond

SL-DRF-20-0614

Research field : Astrophysics
Location :

Direction d’Astrophysique (DAP)

Laboratoire CosmoStat (LCS)

Saclay

Contact :

Martin Kilbinger

Valeria Pettorino

Starting date : 01-10-2020

Contact :

Martin Kilbinger
CEA - DRF/IRFU/DAP/LCS

21753

Thesis supervisor :

Valeria Pettorino
CEA - DRF/IRFU/DAP/LCS

0785502477

Personal web page : www.cosmostat.org/valeria-pettorino

Laboratory link : www.cosmostat.org

More : http://www.cosmostat.org/jobs/xc_dap_dphp

We propose a PhD thesis which builds on the tools and expertise available within the lab, and aims at providing key Tools and results that will be used for the Euclid collaboration and beyond.

The hired PhD candidate within this project will be at the interface between theory and observations to get the best scientific return out of the big investment done in space missions like Euclid, in particular in Europe and by CNES.

The main objectives are:

1) learn how to use existing XC codes (such as COSMOSIS, developed by Martin Kilbinger) and use available data (such as real or simulated data for Euclid) to test modified gravity models beyond LCDM (with supervision of Valeria Pettorino, expert in the field);

2) investigate how large the contribution of XC with spectroscopic galaxy clustering would be, potentially using 3D WL (for which a code has been validated by A. S. Mancini & V. Pettorino);

3) investigate synergies with other probes, such as data from BOSS/eBOSS (of which Vanina Rulhmann-Kleider is expert) and the Cosmic Microwave Background from Planck (of which V.Pettorino is a CORE2 team member and Planck scientist) or next to come ground space / balloon experiments which will provide (during the time of the PhD) polarisation spectra with a better resolution at small scales.

TARGETING GRAVITATIONAL WAVES WITH OPTICAL SURVEYS: SYNERGY BETWEEN EUCLID AND THE CHINESE SPACE STATION TELESCOPE (CSST)

SL-DRF-20-0565

Research field : Astrophysics
Location :

Direction d’Astrophysique (DAP)

Laboratoire CosmoStat (LCS)

Saclay

Contact :

Martin Kilbinger

Starting date : 01-10-2020

Contact :

Martin Kilbinger
CEA - DRF/IRFU/DAP/LCS

21753

Thesis supervisor :

Martin Kilbinger
CEA - DRF/IRFU/DAP/LCS

21753

Personal web page : www.cosmostat.org/kilbinger

Laboratory link : www.cosmostat.org

More : http://www.cosmostat.org/jobs/gw_euclid_csst

The recent direct detections of gravitational waves (GW) from mergers of

massive compact objects has opened a new window to our Universe. The

electro-magnetic (EM) counterpart of the event GW170817 started a new

multi-messenger era for astronomy. Joint GW and EM observations provide a way

to better understand the physics and rate of violent processes of black hole

and neutron star mergers, and the properties of their host galaxies and stellar

populations.



To identify GW transients via quick follow-up observations across the EM

spectrum, galaxy surveys from ultra-violet (UV), optical, to infrared (IR)

wavelengths are of great importance. This PhD project will explore the synergy

and complementarity of two upcoming space missions, the ESA satellite Euclid

(launch in 2022), and CSST, the Chinese Space Station Telescope (planned for

2024). Both missions will cover a large fraction of the extra-galactic sky

with a common area of 15,000 deg^2.

• Artificial intelligence & Data intelligence

• Astrophysics

 

Retour en haut