Gaussian Mixture Models for Blended Photometric Redshifts
  Future cosmological galaxy surveys such as the Large Synoptic Survey
Telescope (LSST) will photometrically observe very large numbers of galaxies.
Without spectroscopy, the redshifts required for the analysis of these data
will need to be inferred using photometric redshift techniques that are
scalable to large sample sizes. The high number density of sources will also
mean that around half are blended. We present a Bayesian photometric redshift
method for blended sources that uses Gaussian mixture models to learn the joint
flux-redshift distribution from a set of unblended training galaxies, and
Bayesian model comparison to infer the number of galaxies comprising a blended
source. The use of Gaussian mixture models renders both of these applications
computationally efficient and therefore suitable for upcoming galaxy surveys.
