  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.
