zBEAMS: A unified solution for supernova cosmology with redshift
  uncertainties
  Supernova cosmology without spectra will be an important component of future
surveys such as LSST. This lack of supernova spectra results in uncertainty in
the redshifts which, if ignored, leads to significantly biased estimates of
cosmological parameters. Here we present a hierarchical Bayesian formalism --
zBEAMS -- that addresses this problem by marginalising over the unknown or
uncertain supernova redshifts to produce unbiased cosmological estimates that
are competitive with supernova data with spectroscopically confirmed redshifts.
zBEAMS provides a unified treatment of both photometric redshifts and host
galaxy misidentification (occurring due to chance galaxy alignments or faint
hosts), effectively correcting the inevitable contamination in the Hubble
diagram. Like its predecessor BEAMS, our formalism also takes care of non-Ia
supernova contamination by marginalising over the unknown supernova type. We
illustrate this technique with simulations of supernovae with photometric
redshifts and host galaxy misidentification. A novel feature of the photometric
redshift case is the important role played by the redshift distribution of the
supernovae.
