Simulation-Based Inference of Strong Gravitational Lensing Parameters
  In the coming years, a new generation of sky surveys, in particular, Euclid
Space Telescope (2022), and the Rubin Observatory's Legacy Survey of Space and
Time (LSST, 2023) will discover more than 200,000 new strong gravitational
lenses, which represents an increase of more than two orders of magnitude
compared to currently known sample sizes. Accurate and fast analysis of such
large volumes of data under a statistical framework is therefore crucial for
all sciences enabled by strong lensing. Here, we report on the application of
simulation-based inference methods, in particular, density estimation
techniques, to the predictions of the set of parameters of strong lensing
systems from neural networks. This allows us to explicitly impose desired
priors on lensing parameters, while guaranteeing convergence to the optimal
posterior in the limit of perfect performance.
