Recovered supernova Ia rate from simulated LSST images

¹ INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, 80131 Napoli, Italy
e-mail: vincenzo.petrecca@inaf.it
² Department of Physics, University of Napoli “Federico II”, Via Cinthia 9, 80126 Napoli, Italy
³ INAF – Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, Padova 35122, Italy
⁴ Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
⁵ Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Mail Number H29, PO Box 218 31122 Hawthorn, VIC, Australia
⁶ Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
⁷ DIRAC Institute, Department of Astronomy, University of Washington, 3910 15th Avenue NE, Seattle, WA 98195, USA
⁸ INFN – Sezione di Napoli, Via Cinthia 9, 80126 Napoli, Italy
⁹ University of Delaware, 210 South College Ave., Newark, DE 19716, USA
¹⁰ Vera C. Rubin Observatory, Tucson, AZ 85719, USA

Received: 19 December 2023
Accepted: 23 February 2024

Abstract

Aims. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors.

Methods. We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg² region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate.

Results. We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.