Photometric Redshift Estimation on SDSS Data Using Random Forests

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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.
4 pages, 4 figures, to be published in Proceedings of ADASS XVII

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Astrophysics

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http://salesiana.dossiersoluciones.com/handle/123456789/142216

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0711.2477
 http://arxiv.org/abs/0711.2477

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arXiv