Mathematics – Logic
Scientific paper
Jan 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21343808m&link_type=abstract
American Astronomical Society, AAS Meeting #213, #438.08; Bulletin of the American Astronomical Society, Vol. 41, p.311
Mathematics
Logic
Scientific paper
Upcoming dark energy experiments will measure a large number of supernovae using broadband filter photometry. Because only Type Ia supernovae can be used for cosmological applications, these experiments face the challenging problem of identifying the type of supernova based on photometric information alone. A subset of Type Ia supernovae will be selected for spectroscopic follow-up, since spectroscopy is the most reliable way of identifying the type of supernova. However, spectroscopy can be time-consuming and expensive. Even those experiments that plan on taking the spectrum of every supernova candidate must select only those candidates that are most likely to be Type Ia's, based on photometric measurements taken before the supernova reaches maximum brightness. It is therefore essential to develop a scheme whereby one can accurately classify supernova using as few photometric measurements as possible. Further, it should work within a triggering scheme in which the purity and efficiency can be well controlled. Here we present such a trigger that uses a probabilistic classification based on photometric measurements. This probability is re-cast in terms of a likelihood ratio that parameterizes the belief that (1) the candidate is consistent with a Type Ia and (2) the supernova candidate has not yet reached maximum brightness. This likelihood ratio is then used within the framework of a sequential analysis where the efficiency and purity can be set a priori to obtain a decision in real time and with as few measurements as possible.
Connolly Brian
Connolly Natalia
Minella Timothy
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