Scalable Bayesian Inference for Finding Strong Gravitational Lenses by Yash Patel et al. on Monday 21 November
Finding strong gravitational lenses in astronomical images allows us to
assess cosmological theories and understand the large-scale structure of the
universe. Previous works on lens detection do not quantify uncertainties in
lens parameter estimates or scale to modern surveys. We present a fully
amortized Bayesian procedure for lens detection that overcomes these
limitations. Unlike traditional variational inference, in which training
minimizes the reverse Kullback-Leibler (KL) divergence, our method is trained
with an expected forward KL divergence. Using synthetic GalSim images and real
Sloan Digital Sky Survey (SDSS) images, we demonstrate that amortized inference
trained with the forward KL produces well-calibrated uncertainties in both lens
detection and parameter estimation.
arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.10479v1
