Year: 2008
Type: article
Abstract: 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.
Source: The Astrophysical Journal
DOI: 10.1086/592591
Institutions: University of Chicago, Fermi National Accelerator Laboratory
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 Chicago, 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
Awards: Center for Cosmological Physics, G154696372, CAREER: Simulations of Galaxy and Cluster Formation: The New Frontiers, COLLABORATIVE RESEARCH: Modeling formation of dwarf galaxies from the Cosmic Renaissance to the Present, G5616713406, G585908603, The Physics Frontier Center of the Kavli Institute for Cosmological Physics, COLLABORATIVE RESEARCH: New Frontiers in Galaxy Formation Modeling, G86560745, G969889393
OpenAlex ID: W2105919768