Princeton University Thunch Talk

Gravitational lensing, which is the effect of the distortion of distant galaxy images through the influence of massive matter densities in the line of sight, holds significant promise in addressing questions about dark matter and dark energy. It reflects the distribution of total matter of the Universe and is therefore a promising probe for cosmological models. In the case where these distortions are small, we call it the weak gravitational lensing regime, and a straightforward mapping exists between the matter distribution projected in the line of sight, called mass-map, and the measured lensing effect. However, when attempting to reconstruct dark matter mass-maps under conditions involving missing data and high noise corruption, this linear inverse problem becomes ill-posed and may lack a meaningful solution without additional prior knowledge. In this talk, I will present how to employ recent breakthroughs in the generative modeling literature that enable the modeling of complex distribution in high-dimensional spaces. We propose in particular a novel methodology to solve high-dimensional ill-posed inverse problems, characterizing the full posterior distribution of the problem. By learning the high dimensional prior from cosmological simulations, we demonstrate that we can reconstruct high-resolution 2D mass-maps alongside uncertainty quantification. Additionally, I will present a new method for cosmic shear estimation based on forward modeling of the observations at the pixel level. This represents a new paradigm for weak lensing measurement, as we no longer rely on galaxy shape measurements.