We present a new measurement of the volumetric rate of Type Ia supernova up to a redshift of 1.7 , using the Hubble Space Telescope ( HST ) GOODS data combined with an additional HST dataset covering the North GOODS field collected in 2004 .
We employ a novel technique that does not require spectroscopic data for identifying Type Ia supernovae ( 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 a Type Ia supernova .
This Bayesian technique can easily be modified to incorporate improved priors on supernova properties , and it is well-suited for future high-statistics supernovae searches in which spectroscopic follow up of all candidates will be impractical .
Here , the method is validated on both ground- and space-based supernova data having some spectroscopic follow up .
We combine our volumetric rate measurements with low redshift supernova data , and fit to a number of possible models for the evolution of the Type Ia supernova rate as a function of redshift .
The data do not distinguish between a flat rate at redshift > 0.5 and a previously proposed model , in which the Type Ia rate peaks at redshift \sim 1 due to a significant delay from star-formation to the supernova explosion .
Except for the highest redshifts , where the signal to noise ratio is generally too low to apply this technique , this approach yields smaller or comparable uncertainties than previous work .