# Gaussian Mixture Models for Blended Photometric Redshifts

## TL;DR

This paper introduces a Bayesian method using Gaussian mixture models to estimate redshifts of blended galaxies in large photometric surveys, enabling scalable and efficient analysis without spectroscopy.

## Contribution

It presents a novel Bayesian approach with Gaussian mixture models for photometric redshift estimation of blended sources, suitable for large-scale surveys.

## Key findings

- Efficient Gaussian mixture model-based redshift inference for blended sources.
- Bayesian model comparison to determine the number of galaxies in blends.
- Method is scalable for upcoming large galaxy surveys.

## Abstract

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.