Augmenting machine learning photometric redshifts with Gaussian mixture models

TL;DR

This paper enhances machine learning photometric redshift estimation by integrating Gaussian Mixture Models to account for galaxy population distributions, leading to more accurate, less biased, and faster redshift predictions in large surveys.

Contribution

It introduces a novel combination of Gaussian Mixture Models with GPz to improve photometric redshift accuracy by modeling population distributions separately.

Findings

Reduced bias in redshift estimates by up to 50%

Improved accuracy in diverse galaxy populations

Faster computation times for redshift estimation

Abstract

Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies - almost all of which must be derived from photometry rather than spectroscopy. In this paper we investigate how using statistical models to understand the populations that make up the colour-magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular we combine the use of Gaussian Mixture Models with the high performing machine learning photo-z algorithm GPz and show that modelling and accounting for the different colour-magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near infrared data in two separate deep fields, where training and test data of different colour-magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.