Photometric SN Ia Candidates from the Three-Year SDSS-II SN Survey Data

TL;DR

This paper presents a method to identify Type Ia supernovae photometrically from SDSS-II data with high efficiency and low contamination, enabling large-scale cosmological studies without extensive spectroscopy.

Contribution

The authors develop and demonstrate a photometric classification technique for SN Ia candidates that achieves over 90% efficiency and low contamination, expanding the potential for cosmological analyses.

Findings

Photometric SN Ia classification efficiency ~91%.

Contamination rate from non-Ia sources ~6%.

Photometric redshift-based Hubble diagram shows minimal bias.

Abstract

We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured, while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with ~91% efficiency and with a contamination of ~6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only ~20 - 40% larger than that of the spectroscopically-confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibit biases that require further investigation for precision cosmology.