A Universal Bayesian Supernova Discriminator For Future Dark Energy Surveys

Details A Universal Bayesian Supernova Discriminator For Future Dark Energy Surveys A Universal Bayesian Supernova Discriminator For Future Dark Energy Surveys

Jan 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21343801c&link_type=abstract

American Astronomical Society, AAS Meeting #213, #438.01; Bulletin of the American Astronomical Society, Vol. 41, p.310

Mathematics

Probability

Scientific paper

Upcoming large-scale ground- and space- based supernova surveys will face a challenge identifying supernova candidates without the use of spectroscopy. Over the past several years, a number of supernova identification schemes have been suggested that rely on photometric information only. Our work proposes a new photometric classification scheme that has a number of advantages over the more well-established color-color or color-magnitude identification techniques. The approach, based on calculating a likelihood ratio, allows one to include all of the systematic and statistical uncertainties of the measurements in a single step. It is also independent of the assumptions on the absolute magnitude of a supernova candidate in any given broadband filter (relying, instead, on the relative distribution of light among a number of filters). Unlike various alternative (Bayesian) probability based photometric identification schemes, the method proposed here does not require knowledge of the complete set of possible astronomical objects that a supernova candidate might conceivably be. The advantages of the technique naturally allows it to be used to pre-select and rank supernova candidates for possible spectroscopic follow-up -- i.e., to be used as a supernova trigger.

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