EIGHT-DIMENSIONAL MID-INFRARED/OPTICAL BAYESIAN QUASAR SELECTION
- Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104 (United States)
- Spitzer Science Center, Caltech, Mail Code 220-6, Pasadena, CA 91125 (United States)
- Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801-3080 (United States)
- Institute of Cosmology and Gravitation, Mercantile House, Hampshire Terrace, University of Portsmouth, Portsmouth, PO1 2EG (United Kingdom)
- Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540 (United States)
- Department of Astronomy and Astrophysics, Pennsylvania State University, 525 Davey Laboratory, University Park, PA 16802 (United States)
- College of Computing, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332 (United States)
- Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218-2686 (United States)
We explore the multidimensional, multiwavelength selection of quasars from mid-infrared (MIR) plus optical data, specifically from Spitzer-Infrared Array Camera (IRAC) and the Sloan Digital Sky Survey (SDSS). Traditionally, quasar selection relies on cuts in two-dimensional color space despite the fact that most modern surveys (optical and IR) are done in more than three bandpasses. In this paper, we apply modern statistical techniques to combined Spitzer MIR and SDSS optical data, allowing up to eight-dimensional (8-D) color selection of quasars. Using a Bayesian selection method, we catalog 5546 quasar candidates to an 8.0 {mu}m depth of 56 {mu}Jy over an area of {approx}24 deg{sup 2}. Roughly 70% of these candidates are not identified by applying the same Bayesian algorithm to 4-color SDSS optical data alone. The 8-D optical+MIR selection on this data set recovers 97.7% of known type 1 quasars in this area and greatly improves the effectiveness of identifying 3.5 < z < 5 quasars which are challenging to identify (without considerable contamination) using MIR data alone. We demonstrate that, even using only the two shortest wavelength IRAC bandpasses (3.6 and 4.5 {mu}m), it is possible to use our Bayesian techniques to select quasars with 97% completeness and as little as 10% contamination (as compared to {approx}60% contamination using color cuts alone). We compute photometric redshifts for our sample; comparison with known objects suggests a photometric redshift accuracy of 93.6% ({delta}z {+-} 0.3), remaining roughly constant when the two reddest MIR bands are excluded. Despite the fact that our methods are designed to find type 1 (unobscured) quasars, as many as 1200 of the objects are type 2 (obscured) quasar candidates. Coupling deep optical imaging data, with deep MIR data, could enable selection of quasars in significant numbers past the peak of the quasar luminosity function (QLF) to at least z {approx} 4. Such a sample would constrain the shape of the QLF both above and below the break luminosity (L* {sub Q}) and enable quasar clustering studies over the largest range of redshift and luminosity to date, yielding significant gains in our understanding of the physics of quasars and their contribution to galaxy evolution.
- OSTI ID:
- 21269244
- Journal Information:
- Astronomical Journal (New York, N.Y. Online), Vol. 137, Issue 4; Other Information: DOI: 10.1088/0004-6256/137/4/3884; Country of input: International Atomic Energy Agency (IAEA); ISSN 1538-3881
- Country of Publication:
- United States
- Language:
- English
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