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    The Extremely Luminous Quasar Survey in the SDSS Footprint. I. Infrared-based Candidate Selection

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    Schindler_2017_ApJ_851_13.pdf
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    Author
    Schindler, Jan-Torge
    Fan, Xiaohui cc
    McGreer, Ian D. cc
    Yang, Qian cc
    Wu, Jin cc
    Jiang, Linhua cc
    Green, Richard
    Affiliation
    Univ Arizona, Steward Observ
    Issue Date
    2017-12-06
    Keywords
    galaxies: nuclei
    quasars: general
    
    Metadata
    Show full item record
    Publisher
    IOP PUBLISHING LTD
    Citation
    The Extremely Luminous Quasar Survey in the SDSS Footprint. I. Infrared-based Candidate Selection 2017, 851 (1):13 The Astrophysical Journal
    Journal
    The Astrophysical Journal
    Rights
    © 2017. The American Astronomical Society. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Studies of the most luminous quasars at high redshift directly probe the evolution of the most massive black holes in the early universe and their connection to massive galaxy formation. However, extremely luminous quasars at high redshift are very rare objects. Only wide-area surveys have a chance to constrain their population. The Sloan Digital Sky Survey (SDSS) has so far provided the most widely adopted measurements of the quasar luminosity function at z > 3. However, a careful re-examination of the SDSS quasar sample revealed that the SDSS quasar selection is in fact missing a significant fraction of z greater than or similar to 3 quasars at the brightest end. We identified the purely optical-color selection of SDSS, where quasars at these redshifts are strongly contaminated by late-type dwarfs, and the spectroscopic incompleteness of the SDSS footprint as the main reasons. Therefore, we designed the Extremely Luminous Quasar Survey (ELQS), based on a novel near-infrared JKW2 color cut using Wide-field Infrared Survey Explorer mission (WISE) AllWISE and 2MASS all-sky photometry, to yield high completeness for very bright (m(i) < 18.0) quasars in the redshift range of 3.0 <= z <= 5.0. It effectively uses random forest machinelearning algorithms on SDSS and WISE photometry for quasar-star classification and photometric redshift estimation. The ELQS will spectroscopically follow-up similar to 230 new quasar candidates in an area of similar to 12,000 deg(2) in the SDSS footprint to obtain a well-defined and complete quasar sample for an accurate measurement of the brightend quasar luminosity function (QLF) at 3.0 <= z <= 5.0. In this paper, we present the quasar selection algorithm and the quasar candidate catalog.
    ISSN
    1538-4357
    DOI
    10.3847/1538-4357/aa9929
    Version
    Final published version
    Sponsors
    NSF [AST 15-15115]; National Key R&D Program of China [2016YFA0400703]; National Science Foundation of China [11533001]; National Aeronautics and Space Administration; National Science Foundation; Alfred P. Sloan Foundation; U.S. Department of Energy Office of Science; Center for High-Performance Computing at the University of Utah; Brazilian Participation Group; Carnegie Institution for Science; Carnegie Mellon University; Chilean Participation Group; French Participation Group; Harvard-Smithsonian Center for Astrophysics; Instituto de Astrofisica de Canarias; Johns Hopkins University; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo; Lawrence Berkeley National Laboratory; Leibniz Institut fur Astrophysik Potsdam (AIP); Max-Planck-Institut fur Astronomie (MPIA Heidelberg); Max-Planck-Institut fur Astrophysik (MPA Garching); Max-Planck-Institut fur Extraterrestrische Physik (MPE); National Astronomical Observatories of China; New Mexico State University; New York University; University of Notre Dame; Observatrio Nacional/MCTI; Ohio State University; Pennsylvania State University; Shanghai Astronomical Observatory; United Kingdom Participation Group; Universidad Nacional Autonoma de Mexico; University of Arizona; University of Colorado Boulder; University of Oxford; University of Portsmouth; University of Utah; University of Virginia; University of Washington; University of Wisconsin; Vanderbilt University; Yale University
    Additional Links
    http://stacks.iop.org/0004-637X/851/i=1/a=13?key=crossref.0fe564d8e606e49c374ae6d28e61ec02
    ae974a485f413a2113503eed53cd6c53
    10.3847/1538-4357/aa9929
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