SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators

TL;DR

This paper introduces an empirical model of Type Ia supernovae that leverages both nearby and distant supernova data to improve distance measurements and spectral analysis, especially at high redshifts.

Contribution

The paper presents a new spectral evolution model for Type Ia supernovae that incorporates variability and improves distance estimation for high-redshift supernovae.

Findings

Enhanced distance measurement accuracy for z>0.8 supernovae.

Effective spectral variability characterization.

Improved photometric redshift estimation methods.

Abstract

We present an empirical model of Type Ia supernovae spectro-photometric evolution with time. The model is built using a large data set including light-curves and spectra of both nearby and distant supernovae, the latter being observed by the SNLS collaboration. We derive the average spectral sequence of Type Ia supernovae and their main variability components including a color variation law. The model allows us to measure distance moduli in the spectral range 2500-8000 A with calculable uncertainties, including those arising from variability of spectral features. Thanks to the use of high-redshift SNe to model the rest-frame UV spectral energy distribution, we are able to derive improved distance estimates for SNe Ia in the redshift range 0.8<z<1.1. The model can also be used to improve spectroscopic identification algorithms, and derive photometric redshifts of distant Type Ia supernovae.