JWST will be launched in 2021, with an important participation from ESA, CNES and CEA for the mid-infrared instrument MIRI. JWST capabilities are revolutionary, compared to the existing state of the art, in terms of resolution and sensitivity over the 1- 30?m wavelength range, where stars and (warm) dust emit their light from galaxies at high redshift. For the first time JWST will provide spatially resolved photometry up to the mid-IR (at least 10?m, with NIRCAM and MIRI) with sub-arcsec resolution. The competitive exploitation of the data for scientific endeavors will require the mastering of the data, deeply understanding the reduction, treatment and developing tools to foster the analysis. I propose a PhD thesis in Saclay as a collaborative effort between experts from ’MICE, the Centre of Expertise for MIRI’, developed at CEA/Irfu/DAp, and with researchers in galaxy formation and evolution. The student will be responsible for developing new high level software for the analysis of resolved imaging data from MIRI and NIRCAM, modeling and understand the resolution, ’pixelization’ and PSF convolution effects. This will include high-level software to create spatially resolved maps of physical parameters (stellar mass, dust attenuation, stellar age, star formation rate) and pixel-by-pixel spectral energy distributions. The student will work on testing and improving the existing MIRI simulator, adapting it to the case of resolved observations of distant galaxies. The results of the efforts will be shared with several of the CEA Saclay groups in the spirit of fostering our expertise and efficiency in the early use of the groundbreaking JWST data. This work will be based on data from our recently approved Early Release Science (ERS) project observing with a suite of JWST instruments (NIRCAM, NIRSPEC, and MIRI) on well-studied cosmological fields. This ERS project is lead by S. Finkelstein at the University of Texas and includes E. Daddi and D. Elbaz from CEA-Saclay among the international teams of proposers. These observations will be among the first delivered by JWST, in parallel with those from GTO teams.

The student will ultimately use the Early Release Science data on cosmological fields to search for ongoing hidden merger events and AGN components resolved inside galaxies, by distinguishing them from the whole galaxy (e.g., nuclear events, or similar), and constraining the growth of inner bulges with passive and/or active stellar populations. This research is based on recent discoveries from our team at the peak of galaxy formation z=1-4. Eventually this research will lead to the first realistic estimates of the relevance of these widely discussed and hot topics.

One of the major challenges of astrophysics is to understand how galaxies assemble their mass, give birth to their supermassive stars and black holes over time, and how this assembly depends on their internal evolution and/or external factors such as dark matter haloes and galactic fusions. To date, our understanding of the cosmic history of star formation remains largely incomplete over the key period of the 3 billion years following reionization, i.e. between z~6 - when galaxies had formed less than 1% of their current stars - until the time of the star formation peak around z~1.5. ?During this thesis, the student will benefit from a unique set of data - mainly from the ALMA interferometer and the James Webb Space Telescope (JWST) - that will allow him to quantify this period in the history of star formation activity, mass growth and the morphological evolution of galaxies. Overcoming the uncertainty about this era will therefore have a major impact on our understanding of the formation of structures in the universe, and even our cosmology, and this is one of the major objectives of our JWST program.

Clusters of galaxies are the most massive entities in the universe. As such, they constitute powerful cosmological probes.

The XLL survey is the largest programme of the European satellite XMM (X-ray band). It has enabled the detection of several hundreds of galaxy clusters.

The goal of the PhD is to perform the cosmological analysis of the cluster sample by using artificial intelligence techniques : deep learning and convolutive neural networks.