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

V. Ashley Villar

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arXiv · 2211.04480

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

V. Ashley Villar

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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.

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V. Ashley Villar. 2022-11-08. Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows. https://arxiv.org/abs/2211.04480

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