ArborZ: PHOTOMETRIC REDSHIFTS USING BOOSTED DECISION TREES

Journal Article · Tue Jun 01 00:00:00 EDT 2010 · Astrophysical Journal

DOI:https://doi.org/10.1088/0004-637X/715/2/823· OSTI ID:21450942

Gerdes, David W; Sypniewski, Adam J; McKay, Timothy A; Hao, Jiangang; Weis, Matthew R ^[1]; Wechsler, Risa H

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1. Department of Physics, University of Michigan, Ann Arbor, MI 48109 (United States)

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 (SDSS) 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.

• OSTI ID: 21450942
• Journal Information: Astrophysical Journal, Vol. 715, Issue 2; Other Information: DOI: 10.1088/0004-637X/715/2/823; ISSN 0004-637X
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTROPHYSICS
COSMOLOGY AND ASTRONOMY
ALGORITHMS
DATA ANALYSIS
DECISION TREE ANALYSIS
GALAXIES
NONLUMINOUS MATTER
PROBABILITY DENSITY FUNCTIONS
RED SHIFT
UNIVERSE
FUNCTIONS
MATHEMATICAL LOGIC
MATTER