BibTeX

@article{2510.14202v1,
Author        = {Edgar P. Vidal and Alexander T. Gagliano and Carolina Cuesta-Lazaro},
Title         = {Hierarchical Simulation-Based Inference of Supernova Power Sources and
  their Physical Properties},
Eprint        = {2510.14202v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Time domain surveys such as the Vera C. Rubin Observatory are projected to
annually discover millions of astronomical transients. This and complementary
programs demand fast, automated methods to constrain the physical properties of
the most interesting objects for spectroscopic follow up. Traditional
approaches to likelihood-based inference are computationally expensive and
ignore the multi-component energy sources powering astrophysical phenomena. In
this work, we present a hierarchical simulation-based inference model for
multi-band light curves that 1) identifies the energy sources powering an event
of interest, 2) infers the physical properties of each subclass, and 3)
separates physical anomalies in the learned embedding space. Our architecture
consists of a transformer-based light curve summarizer coupled to a
flow-matching regression module and a categorical classifier for the physical
components. We train and test our model on $\sim$150k synthetic light curves
generated with $\texttt{MOSFiT}$. Our network achieves a 90% classification
accuracy at identifying energy sources, yields well-calibrated posteriors for
all active components, and detects rare anomalies such as tidal disruption
events (TDEs) through the learned latent space. This work demonstrates a
scalable joint framework for population studies of known transients and the
discovery of novel populations in the era of Rubin.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.14202v1},
File          = {2510.14202v1.pdf}
}