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. A particular effort of our team at APC brings on 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. 

Nevertheless, new challenges arise, 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 Bayesian neural network to separate overlapping galaxies before measuring their shape, bringing an improvement to the use of one of the surveys alone (as we get the complexity but also the power of a multi-resolution and multiwavelength approach to the problem). We have demonstrated the feasibility of this approach in a configuration with several simulated galaxies per image ([2]). 

This approach is still further tested on more realistic configurations and the main topic to pursue during the offered internship would be the quality assessment of the deblending procedure on realistic galaxy fields simulations, with a focus on the LSST-Euclid combination. It could be followed by a PhD thesis more focused on cosmic shear analyses.