A Probabilistic Approach to Classifying Supernovae Using Photometric Information

Details A Probabilistic Approach to Classifying Supernovae Using Photometric Information A Probabilistic Approach to Classifying Supernovae Using Photometric Information

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aas...209.9002c&link_type=abstract

2007 AAS/AAPT Joint Meeting, American Astronomical Society Meeting 209, #90.02; Bulletin of the American Astronomical Society, V

Mathematics

Probability

Scientific paper

We present a novel method for determining the probability that a supernova candidate belongs to a known supernova type (such as Ia, Ibc, IIL, etc.), using its photometric information alone. It is validated with Monte Carlo, and both spaceand groundbased data. We examine the application of the method to well-sampled as well as poorly sampled supernova light curves. 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.

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