ArborZ: Photometric Redshifts Using Boosted Decision Trees

Details ArborZ: Photometric Redshifts Using Boosted Decision Trees ArborZ: Photometric Redshifts Using Boosted Decision Trees

2009-08-27

arxiv.org/abs/0908.4085v1

Astrophysical Journal 715 (2010) 823-832

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

10 pages, 13 figures, submitted to ApJ

Scientific paper

10.1088/0004-637X/715/2/823

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of Boosted Decision Trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single "best estimate" and error, and also provides a photo-z quality figure-of-merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.

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