Optimization of Optical Cross-Correlation Filters for Type Ia Supernova Classification and Redshift Estimation

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Scientific paper

Upcoming wide-field optical surveys, such as the Dark Energy Survey, are expected to detect thousands of Type Ia supernovae. Classification of these supernovae and estimation of their redshift has traditionally required time-consuming spectroscopic followup. Optical cross-correlation filters (Scolnic et al., 2009) have the potential to be an efficient, low-cost alternative. This method employs a pair of comb filters optimized for sensitivity to features in the SNe Ia spectrum that are not present in supernovae of other types. We have used a Markov Chain Monte Carlo to optimize the parameters of the two cross-correlation filters, using observed supernova spectra for validation. We present results for the redshift estimation accuracy and classification efficiency.

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