Random Forests for Photometric Redshifts

Details Random Forests for Photometric Redshifts Random Forests for Photometric Redshifts

Mar 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010apj...712..511c&link_type=abstract

The Astrophysical Journal, Volume 712, Issue 1, pp. 511-515 (2010).

Astronomy and Astrophysics

Astronomy

7

Galaxies: Distances And Redshifts, Methods: Data Analysis, Methods: Statistical, Techniques: Photometric

Scientific paper

The main challenge today in photometric redshift estimation is not in the accuracy but in understanding the uncertainties. We introduce an empirical method based on Random Forests to address these issues. The training algorithm builds a set of optimal decision trees on subsets of the available spectroscopic sample, which provide independent constraints on the redshift of each galaxy. The combined forest estimates have intriguing statistical properties, notable among which are Gaussian errors. We demonstrate the power of our approach on multi-color measurements of the Sloan Digital Sky Survey.

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