To optimise the optical performances of a future space instrument dedicated to CMB polarisation observations, it will be very interesting to reduce the physical size of the focal plane without decreasing the number of detectors. An elegant way to reach such architecture would be to use multichroic detectors that are sensitive to multiple frequency bands. A large bandwidth antenna could be used to feed multiple detectors (TESs or KIDs) through different bandpass filters.

Inflation is an important theory of the primordial universe that explains amongst other things the origin of the fluctuations observed in the cosmic microwave background (CMB) and of the large-scale structure of galaxy clusters. However, there are many different inflation models and one of the challenges of modern cosmology is to find ways to distinguish these models at the level of their predictions for observables, in order to confront them with experimental data.

The goal of this thesis is to apply deep learning to the detection of galaxy clusters in astronomical surveys.  Clusters are the most massive objects in the universe, hosting hundreds of galaxies and a gaseous intra-cluster medium (ICM) at temperatures of tens of millions of Kelvin.  They are detected as galaxy over-densities in optical/near-infrared (NIR) imaging, through the X-ray emission of their ICM or via the Sunyaev-Zeldovich (SZ) effect, a distortion of the cosmic microwave background spectrum generated by photon-electron scattering in the ICM.  Clusters serve as powerful probes of

Many popular theories of the early Universe predict that gravitational waves were generated in the very first moments of its life. These theories generally invoke new physics from beyond the standard model of the particle physics and the primordial gravitational waves are believed to carry clues about the nature of those new physics laws. Detecting the primordial gravitational waves would thus have revolutionary impact on our understanding of cosmology and fundamental physics.