A Bayesian Approach to Classifying Supernovae With Color
  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.
