Supernova Simulations and Strategies For the Dark Energy Survey
  We present an analysis of supernova light curves simulated for the upcoming
Dark Energy Survey (DES) supernova search. The simulations employ a code suite
that generates and fits realistic light curves in order to obtain distance
modulus/redshift pairs that are passed to a cosmology fitter. We investigated
several different survey strategies including field selection, supernova
selection biases, and photometric redshift measurements. Using the results of
this study, we chose a 30 square degree search area in the griz filter set. We
forecast 1) that this survey will provide a homogeneous sample of up to 4000
Type Ia supernovae in the redshift range 0.05<z<1.2, and 2) that the increased
red efficiency of the DES camera will significantly improve high-redshift color
measurements. The redshift of each supernova with an identified host galaxy
will be obtained from spectroscopic observations of the host. A supernova
spectrum will be obtained for a subset of the sample, which will be utilized
for control studies. In addition, we have investigated the use of combined
photometric redshifts taking into account data from both the host and
supernova. We have investigated and estimated the likely contamination from
core-collapse supernovae based on photometric identification, and have found
that a Type Ia supernova sample purity of up to 98% is obtainable given
specific assumptions. Furthermore, we present systematic uncertainties due to
sample purity, photometric calibration, dust extinction priors, filter-centroid
shifts, and inter-calibration. We conclude by estimating the uncertainty on the
cosmological parameters that will be measured from the DES supernova data.
