Gaussian Mixture Models for Blended Photometric Redshifts

Understand

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.