Core-collapse supernova parameter estimation with the upcoming Vera C. Rubin Observatory

¹ INAF – Osservatorio Astronomico di Roma, Via di Frascati 33, I-00078 Monteporzio Catone, Italy
² Università Tor Vergata, Dipartimento di Fisica, Via della Ricerca Scientifica 1, I-00133 Rome, Italy
³ Dipartimento di Fisica “Ettore Pancini”, Università di Napoli Federico II, Via Cinthia 9, 80126 Naples, Italy
⁴ INAF – Osservatorio Astronomico di Capodimonte, Via Moiariello 16, I-80131 Naples, Italy
⁵ Universitá La Sapienza, Dipartimento di Fisica, Piazzale Aldo Moro 2, I-00185 Rome, Italy
⁶ INFN, Sezione di Roma, 00133 Rome, Italy

^⋆ Corresponding author: andrea.simongini@inaf.it

Received: 25 March 2025
Accepted: 29 May 2025

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, capable of detecting faint sources down to r∼27.5 mag, 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), spanning a wide range of redshifts, with an expected rate of 6.8×10⁻⁵ SNe Mpc⁻³ yr⁻¹. 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 supernovae (CCSNe). The explosions were modeled using the radiative transfer code STELLA, and each event was provided with a value of redshift, extinction, cadence, explosion energy, nickel yield, and progenitor mass. We analyzed this dataset with the software CASTOR, which enables the reconstruction of synthetic light curves and spectra via a machine learning technique that allows one to retrieve the complete parameter map of a SN. For each parameter we compared the observed and the true values, determining how LSST light curves alone will contribute to characterize the progenitor and the explosion. Our results indicate that LSST alone will not suffice for a comprehensive and precise characterization of progenitor properties and explosion parameters. The limited spectral coverage of LSST light curves (in most cases) does not allow for the accurate estimation of bolometric luminosity, and consequently, of the explosion energy and nickel yield. Additionally, the redshift-absorption degeneracy is difficult to resolve without supplementary information. These findings suggest that for the most interesting SNe, complementary follow-up observations using spectrographs and optical facilities (particularly in the infrared bands) will be essential for accurate parameter determination.