Photometric Redshift Estimation on SDSS Data Using Random Forests
Astrophysics
2007-11-16 v1
Abstract
Given multiband photometric data from the SDSS DR6, we estimate galaxy redshifts. We employ a Random Forest trained on color features and spectroscopic redshifts from 80,000 randomly chosen primary galaxies yielding a mapping from color to redshift such that the difference between the estimate and the spectroscopic redshift is small. Our methodology results in tight RMS scatter in the estimates limited by photometric errors. Additionally, this approach yields an error distribution that is nearly Gaussian with parameter estimates giving reliable confidence intervals unique to each galaxy photometric redshift.
Keywords
Cite
@article{arxiv.0711.2477,
title = {Photometric Redshift Estimation on SDSS Data Using Random Forests},
author = {Samuel Carliles and Tamás Budavári and Sebastien Heinis and Carey Priebe and Alexander Szalay},
journal= {arXiv preprint arXiv:0711.2477},
year = {2007}
}
Comments
4 pages, 4 figures, to be published in Proceedings of ADASS XVII