A Bayesian Approach to Classifying Supernovae With Color

Abstract

Upcoming large-scale ground- and space- based supernova surveys will face a challenge identifying supernova candidates largely without the use of spectroscopy. Over the past several years, a number of supernova identification schemes have been proposed that rely on photometric information only. Some of these schemes use color-color or color-magnitude diagrams; others simply fit supernova data to models. Both of these approaches suffer a number of drawbacks partially addressed in the so-called Bayesian-based supernova classification techniques. However, Bayesian techniques are also problematic in that they typically require that the supernova candidate be one of a known set of supernova types. This presents a number of problems, the most obvious of which is that there are bound to be objects that do not conform to any presently known model in large supernova candidate samples. We propose a new photometric classification scheme that uses a Bayes factor based on color in order to identify supernovae by type. This method does not require knowledge of the complete set of possible astronomical objects that could mimic a supernova signal. Further, as a Bayesian approach, it accounts for all systematic and statistical uncertainties of the measurements in a single step. To illustrate the use of the technique, we apply it to a simulated dataset for a possible future large-scale space-based Joint Dark Energy Mission and demonstrate how it could be used to identify Type Ia supernovae. The method's utility in pre-selecting and ranking supernova candidates for possible spectroscopic follow-up -- i.e., its usage as a supernova trigger -- will be briefly discussed.