Type Ia supernova rates with HST ACS

We employ a novel technique that does not require spectroscopic data for identifying SNe Ia (although spectroscopic measurements of redshifts are used for over half the sample); instead, we employ a Bayesian approach using only photometric data to calculate the probability that an object is an SN Ia.

This Bayesian technique can easily be modified to incorporate improved priors on SN properties, and it is well-suited for future high-statistics SN searches in which spectroscopic follow-up of all candidates will be impractical. Here the method is validated on both ground- and space-based SN data having some spectroscopic follow-up. We combine our volumetric rate measurements with low-redshift SN data and fit to a number of possible models for the evolution of the SN Ia rate as a function of redshift.