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

VA Villar - arXiv preprint arXiv:2211.04480, 2022 - arxiv.org
Astrophysics paper astro-ph.HE Suggest

… 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…

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BibTeX

@article{2211.04480v1,
Author        = {V. Ashley Villar},
Title         = {Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin
  Observatory Using Normalizing Flows},
Eprint        = {2211.04480v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
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.},
Year          = {2022},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2211.04480v1},
File          = {2211.04480v1.pdf}
}

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