GAz: A Genetic Algorithm for Photometric Redshift Estimation

TL;DR

This paper introduces GAz, a genetic algorithm-based method for estimating galaxy redshifts from photometric data, achieving competitive accuracy with simple polynomial models that are easy to use and generalize well across datasets.

Contribution

The paper presents a novel genetic algorithm approach for photometric redshift estimation that models high-order polynomials efficiently, outperforming traditional methods in simplicity and generalization.

Findings

Achieves $\sigma_{ ext{rms}}=0.0408$ for $0.4<z<0.7$

Competitive with state-of-the-art methods

Code is publicly available for broad use

Abstract

We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimisation of a small number of terms. We find a $\sigma_{\text{rms}}=0.0408\pm 0.0006$ for redshifts in the range $0.4<z< 0.7$. These results are competitive with the current state-of-the-art but can be presented simply as a polynomial which does not require the user to run any code. We demonstrate that the method generalises well to other data sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For other datasets or applications the code has been made available at https://github.com/rbrthogan/GAz.