Core-collapse supernova parameter estimation with the upcoming Vera C. Rubin Observatory
Andrea Simongini, F. Ragosta, I. Di Palma, S. Piranomonte

TL;DR
The paper evaluates LSST's capability to estimate core-collapse supernova parameters using simulated light curves, finding that LSST alone is insufficient for precise characterization and emphasizing the need for follow-up observations.
Contribution
It introduces a machine learning-based method to analyze LSST supernova light curves and assesses its effectiveness in parameter estimation.
Findings
LSST alone cannot accurately determine progenitor and explosion parameters.
Limited spectral coverage hampers precise bolometric luminosity and explosion energy estimates.
Follow-up observations are necessary for comprehensive supernova characterization.
Abstract
The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to revolutionize time-domain optical astronomy as we know it. With its unprecedented depth, the LSST will survey the southern hemisphere sky, generating nearly 32 trillion observations over its nominal 10-year operation. Among these, approximately 10 million will be supernovae (SNe). These observations will uniquely characterize the SN population, enabling studies of known and rare SN types, detailed parameterization of their light curves, deep searches for new SN progenitor populations, the discovery of strongly lensed SNe, and the compilation of a large, well-characterized sample of superluminous SNe. We analyzed a sample of 22663 simulations of LSST light curves for core collapse SNe (CCSNe), modeled using the radiative transfer code STELLA. We analyzed this dataset with the software CASTOR, which enables…
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