GAz: A Genetic Algorithm for Photometric Redshift Estimation

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.

v2: 11 pages, 11 figures, extended analysis, matches version to be published in MNRAS

References in corpus (8)

• EAZY: A Fast, Public Photometric Redshift Code
• The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS
• The 2dF-SDSS LRG and QSO (2SLAQ) Luminous Red Galaxy Survey
• MegaZ-LRG: A photometric redshift catalogue of one million SDSS Luminous Red Galaxies
• Low Resolution Spectral Templates For Galaxies From 0.2 -- 10 microns
• A catalogue of photometric redshifts for the SDSS-DR9 galaxies
• Genetic Algorithms and the Search for Viable String Vacua
• Impact of sub-solar metallicities on photometric redshifts

Cited by in corpus (3)

• WISE x SuperCOSMOS photometric redshift catalog: 20 million galaxies over 3pi steradians
• Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry
• Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network