Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows

Abstract

The Vera Rubin Observatory, set to begin observations in mid-2024, will increase our discovery rate of supernovae to well over one million annually. There has been a significant push to develop new methodologies to identify, classify and ultimately understand the millions of supernovae discovered with the Rubin Observatory. Here, we present the first simulation-based inference method using normalizing flows, trained to rapidly infer the parameters of toy supernovae model in multivariate, Rubin-like datastreams. We find that our method is well-calibrated compared to traditional inference methodologies (specifically MCMC), requiring only one-ten-thousandth of the CPU hours during test time.