I'm a CNES fellow working at LAM (France) in collaboration with Véronique Buat and Samuel Boissier. In October 2018, I will start a CNRS permanent position at LAM.
My research lies in the framework of galaxy evolution. It is well-known that galaxies show a dichotomy in both morphology and colors. They are separated between the group of blue star-forming galaxies and the group of red and "dead" ones. One of the main question of galaxy evolution is to understand how galaxies evolve from the star-forming group to the passive one.
To tackle this problem I study the star formation history of galaxies by modeling their spectral energy distribution (SED). I especially focuss on the recent star formation history of galaxies using the versatility of the SED fitting code CIGALE. With the large amount of galaxies observed as well as the large wavelength coverage of these observations, we reached a point where we need robust statistical tools to understand the results of our fitting. I'm part of a collaborative effort between astrophysicists and statisticians to apply deep learning to SED fitting.
Ten years ago, the discovery of the tight relation linking the star formation rate (SFR) and stellar mass of star-forming galaxies opened a new window in our understanding of galaxy evolution. The main consequence of this relation is that galaxies are forming the bulk of their stars through steady-state processes rather than violent episodes of star formation. However, the evolutionary path of galaxies on the SFR-M* plane is still debated.
Each physical process taking place in a galaxy leaves its imprint on its spectral energy distribution (SED). Combining multi-wavelength data over the entire spectrum is the only way to break the degeneracies and model the different physical processes. The approach I use is UV-to-radio broad band SED fitting with a particular interest in the AGN/host SED decomposition. I'm involved in the development of the new CIGALE SED modeling code.