Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

¹ McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
² Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), Physics and Astronomy Department, University of Pittsburgh, 3941 O’Hara St, Pittsburgh, PA 15260, USA
³ SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025, USA
⁴ Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France
⁵ CENTRA/COSTAR, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal
⁶ Independent Researcher, Ingolstadt, Germany
⁷ Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA
⁸ CENTRA/SIM, Faculdade de Ciências, Universidade de Lisboa, Ed. C8, Campo Grande, 1749-016 Lisboa, Portugal
⁹ Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
¹⁰ Department of Computer Science, University of California Irvine, Irvine, CA, USA
¹¹ Physics Department, Brookhaven National Laboratory, Upton, NY 11973, USA
¹² Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
¹³ Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain

^★ Corresponding author; aimalz@nyu.edu

Received: 12 May 2023
Accepted: 19 April 2024

Abstract

Context. When selecting a light curve classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process.

Aims. This study tests the assumption that light curve classification metrics are an appropriate proxy for cosmology metrics.

Methods. We emulated photometric SN Ia cosmology light curve samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates.

Results. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter.

Conclusions. Based on these findings, we discourage any exclusive reliance on light curve classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.