Orateur
Connor Stone
(Université de Montréal)
Description
Score Based Diffusion models have emerged as a powerful tool for high dimensional Bayesian analysis. Here we present work done at the Université de Montréal to analyze strong gravitational lenses. As a first step, we sample robust posteriors over high resolution point spread function models to represent the image distortions in the Hubble Space Telescope. We then perform source reconstruction for strong gravitational lenses, effectively undoing the warping caused by gravitational lensing. These methods perform extraordinarily well and set a new standard for state of the art analysis of these systems. Our posterior samples are simultaneously higher likelihood and visually indistinguishable from unlensed galaxies.
Author
Connor Stone
(Université de Montréal)
