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We present a new measurement of the volumetric rate of Type Ia supernova upto a redshift of 1.7, using the Hubble Space Telescope (HST) GOODS datacombined with an additional HST dataset covering the North GOODS fieldcollected in 2004. We employ a novel technique that does not requirespectroscopic data for identifying Type Ia supernovae (although spectroscopicmeasurements of redshifts are used for over half the sample); instead we employa Bayesian approach using only photometric data to calculate the probabilitythat an object is a Type Ia supernova. This Bayesian technique can easily bemodified to incorporate improved priors on supernova properties, and it iswell-suited for future high-statistics supernovae searches in whichspectroscopic follow up of all candidates will be impractical. Here, the methodis validated on both ground- and space-based supernova data having somespectroscopic follow up. We combine our volumetric rate measurements with lowredshift supernova data, and fit to a number of possible models for theevolution of the Type Ia supernova rate as a function of redshift. The data donot distinguish between a flat rate at redshift > 0.5 and a previously proposedmodel, in which the Type Ia rate peaks at redshift >1 due to a significantdelay from star-formation to the supernova explosion. Except for the highestredshifts, where the signal to noise ratio is generally too low to apply thistechnique, this approach yields smaller or comparable uncertainties thanprevious work.

A New Determination of the High Redshift Type Ia Supernova Rates with the Hubble Space Telescope Advanced Camera for Surveys