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The Vera Rubin Observatory, set to begin observations in mid-2024, willincrease 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 withthe Rubin Observatory. Here, we present the first simulation-based inferencemethod using normalizing flows, trained to rapidly infer the parameters of toysupernovae model in multivariate, Rubin-like datastreams. We find that ourmethod is well-calibrated compared to traditional inference methodologies(specifically MCMC), requiring only one-ten-thousandth of the CPU hours duringtest time.

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