Analytic Photometric Redshift Estimator for Type Ia Supernovae From the Large Synoptic Survey Telescope

TL;DR

This paper presents an improved analytic method for estimating photometric redshifts of Type Ia supernovae, achieving high accuracy and low outlier rates using simulated LSST data, which enhances cosmological measurements from supernova surveys.

Contribution

The paper introduces a significantly improved analytic photo-z estimator for SNe Ia, validated on large simulated datasets, providing high-precision redshift estimates crucial for cosmology.

Findings

Achieves 2% accuracy in photometric redshifts

Bias in z_phot is near zero, less than 10^{-4}

Outlier fraction below 0.33% with specified error thresholds

Abstract

Accurate and precise photometric redshifts (photo-z's) of Type Ia supernovae (SNe Ia) can enable the use of SNe Ia, measured only with photometry, to probe cosmology. This dramatically increases the science return of supernova surveys planned for the Large Synoptic Survey Telescope (LSST). In this paper we describe a significantly improved version of the simple analytic photo-z estimator proposed by Wang (2007) and further developed by Wang, Narayan, and Wood-Vasey (2007). We apply it to 55,422 simulated SNe Ia generated using the SNANA package with the LSST filters. We find that the estimated errors on the photo-z's, \sigma_{z_{phot}}/(1+z_{phot}), can be used as filters to produce a set of photo-z's that have high precision, accuracy, and purity. Using SN Ia colors as well as SN Ia peak magnitude in the i band, we obtain a set of photo-z's with 2 percent accuracy (with \sigma(z_{phot}-z_{spec})/(1+z_{spec}) = 0.02), a bias in z_{phot} (the mean of z_{phot}-z_{spec}) of -9 X 10^{-5}, and an outlier fraction (with |(z_{phot}-z_{spec})/(1+z_{spec})|>0.1) of 0.23 percent, with the requirement that \sigma_{z_{phot}}/(1+z_{phot})<0.01. Using the SN Ia colors only, we obtain a set of photo-z's with similar quality by requiring that \sigma_{z_{phot}}/(1+z_{phot})<0.007; this leads to a set of photo-z's with 2 percent accuracy, a bias in z_{phot} of 5.9 X 10^{-4}, and an outlier fraction of 0.32 percent.