A Probabilistic Approach to Classifying Supernovae Using Photometric Information

Abstract

This paper presents a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, \emph{etc.}), using its photometric information alone. It is validated with Monte Carlo, and both space- and ground- based data. We examine the application of the method to well-sampled as well as poorly sampled supernova light curves and investigate to what extent the best currently available supernova models can be used for typing supernova candidates. Central to the method is the assumption that a supernova candidate belongs to a group of objects that can be modeled; we therefore discuss possible ways of removing anomalous or less well understood events from the sample. This method is particularly advantageous for analyses where the purity of the supernova sample is of the essence, or for those where it is important to know the number of the supernova candidates of a certain type (\emph{e.g.}, in supernova rate studies).