Photometric Supernova Cosmology with BEAMS and SDSS-II
  Supernova cosmology without spectroscopic confirmation is an exciting new
frontier which we address here with the Bayesian Estimation Applied to Multiple
Species (BEAMS) algorithm and the full three years of data from the Sloan
Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian
framework for using data from multiple species in statistical inference when
one has the probability that each data point belongs to a given species,
corresponding in this context to different types of supernovae with their
probabilities derived from their multi-band lightcurves. We run the BEAMS
algorithm on both Gaussian and more realistic SNANA simulations with of order
10^4 supernovae, testing the algorithm against various pitfalls one might
expect in the new and somewhat uncharted territory of photometric supernova
cosmology. We compare the performance of BEAMS to that of both mock
spectroscopic surveys and photometric samples which have been cut using typical
selection criteria. The latter typically are either biased due to contamination
or have significantly larger contours in the cosmological parameters due to
small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae
with host spectroscopic redshifts. In this case, BEAMS reduces the area of the
(\Omega_m,\Omega_\Lambda) contours by a factor of three relative to the case
where only spectroscopically confirmed data are used (297 supernovae). In the
case of flatness, the constraints obtained on the matter density applying BEAMS
to the photometric SDSS-II data are \Omega_m(BEAMS)=0.194\pm0.07. This
illustrates the potential power of BEAMS for future large photometric supernova
surveys such as LSST.
