SALT: a Spectral Adaptive Light curve Template for Type Ia Supernovae

TL;DR

This paper introduces SALT, a new spectral adaptive light curve template for Type Ia Supernovae, improving distance measurements by modeling luminosity variations across multiple colors and phases.

Contribution

The paper presents a novel empirical model for SN Ia light curves that enhances distance estimation accuracy using multi-color data.

Findings

Achieved a dispersion of 0.16 +- 0.05 in distance measurements with U- and B-band data.

Model performs comparably or better than existing methods in measuring supernova distances.

Successfully tested the model on independent supernova samples.

Abstract

We present a new method to parameterize Type Ia Supernovae (SN Ia) multi-color light curves. The method was developed in order to analyze the large number of SN Ia multi-color light curves measured in current high-redshift projects. The technique is based on empirically modeling SN Ia luminosity variations as a function of phase, wavelength, a shape parameter, and a color parameter. The model is trained with a sample of well measured nearby SN Ia and then tested with an independent set of supernovae by building an optimal luminosity distance estimator combining the supernova rest-frame luminosity, shape parameter and color reconstructed with the model. The distances we measure using B- and V-band data show a dispersion around the Hubble line comparable or lower than obtained with other methods. With this model, we are able to measure distances using U- and B-band data with a dispersion around the Hubble line of 0.16 +- 0.05.