期刊
ASTROPHYSICAL JOURNAL
卷 689, 期 2, 页码 709-720出版社
IOP PUBLISHING LTD
DOI: 10.1086/592591
关键词
galaxies: distances and redshifts; galaxies: photometry
资金
- Kavli Institute for Cosmological Physics at the University of Chicago [NSF PHY-0114422, NSF PHY-0551142]
- Kavli Foundation
- founder Fred Kavli
- NSF [AST 02-39759, AST 05-07666, AST 07-08154]
- Department of Energy
- DOE
- Fermi Research Alliance
- LLC [DE-AC02-07CH11359]
- Alfred P. Sloan Foundation
- Participating Institutions
- National Science Foundation
- US Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
Photometric redshift (photo-z) estimates are playing an increasingly important role in extragalactic astronomy and cosmology. Crucial to many photo-z applications is the accurate quantification of photometric redshift errors and their distributions, including identification of likely catastrophic failures in photo-z estimates. We consider several methods of estimating photo-z errors, and propose new training-set based error estimators based on spectroscopic training set data. Using data from the Sloan Digital Sky Survey and simulations of the Dark Energy Survey as examples, we show that this method provides a robust, relatively unbiased estimate of photo-z errors. We show that culling objects with large, accurately estimated photo-z errors from a sample can reduce the incidence of catastrophic photo-z failures.
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