Year: 2010
Type: article
Abstract: 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.
Source: The Astrophysical Journal
Authors: David W. Gerdes, Adam J. Sypniewski, Timothy A. McKay, Jiangang Hao, Matthew R. Weis, Risa H. Wechsler, Michael T. Busha
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Princeton University, Ohio State University, University of Washington, Johns Hopkins University, University of Pittsburgh, Case Western Reserve University, Drexel University, Universität Basel, University of Portsmouth, New Mexico State University, Chinese Academy of Sciences, Max-Planck-Gesellschaft, Max-Planck-Institut für Astronomie, U.S. Naval Observatory, Fermilab, Los Alamos National Laboratory
OpenAlex ID: W2054327392