Bayesian HIGH-REDSHIFT QUASAR CLASSIFICATION from OPTICAL and MID-IR PHOTOMETRY

Gordon T. Richards, Adam D. Myers, Christina M. Peters, Coleman M. Krawczyk, Greg Chase, Nicholas P. Ross, Xiaohui Fan, Linhua Jiang, Mark Lacy, Ian D. McGreer, Jonathan R. Trump, Ryan N. Riegel

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

We identify 885,503 type 1 quasar candidates to i ≲ 22 using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer (WISE) "AllWISE" data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type 1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with 3.5 < z > 5 (of which 6779 are new photometric candidates). Our algorithm is more complete to z > 3.5 than the traditional mid-IR selection "wedges" and to 2.2 <z < 3.5 quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggest that the resulting catalog is relatively complete to known quasars and is identifying new high-z quasars at z > 3. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.

Original languageEnglish (US)
Article number39
JournalAstrophysical Journal, Supplement Series
Volume219
Issue number2
DOIs
StatePublished - Aug 1 2015

Fingerprint

quasars
Wide-field Infrared Survey Explorer
photometry
machine learning
Space Infrared Telescope Facility
oscillation
wedges
catalogs
baryons
education
luminosity
oscillations

Keywords

  • catalogs
  • infrared: galaxies
  • methods: statistical
  • quasars: general

ASJC Scopus subject areas

  • Space and Planetary Science
  • Astronomy and Astrophysics

Cite this

Richards, G. T., Myers, A. D., Peters, C. M., Krawczyk, C. M., Chase, G., Ross, N. P., ... Riegel, R. N. (2015). Bayesian HIGH-REDSHIFT QUASAR CLASSIFICATION from OPTICAL and MID-IR PHOTOMETRY. Astrophysical Journal, Supplement Series, 219(2), [39]. https://doi.org/10.1088/0067-0049/219/2/39

Bayesian HIGH-REDSHIFT QUASAR CLASSIFICATION from OPTICAL and MID-IR PHOTOMETRY. / Richards, Gordon T.; Myers, Adam D.; Peters, Christina M.; Krawczyk, Coleman M.; Chase, Greg; Ross, Nicholas P.; Fan, Xiaohui; Jiang, Linhua; Lacy, Mark; McGreer, Ian D.; Trump, Jonathan R.; Riegel, Ryan N.

In: Astrophysical Journal, Supplement Series, Vol. 219, No. 2, 39, 01.08.2015.

Research output: Contribution to journalArticle

Richards, GT, Myers, AD, Peters, CM, Krawczyk, CM, Chase, G, Ross, NP, Fan, X, Jiang, L, Lacy, M, McGreer, ID, Trump, JR & Riegel, RN 2015, 'Bayesian HIGH-REDSHIFT QUASAR CLASSIFICATION from OPTICAL and MID-IR PHOTOMETRY', Astrophysical Journal, Supplement Series, vol. 219, no. 2, 39. https://doi.org/10.1088/0067-0049/219/2/39
Richards, Gordon T. ; Myers, Adam D. ; Peters, Christina M. ; Krawczyk, Coleman M. ; Chase, Greg ; Ross, Nicholas P. ; Fan, Xiaohui ; Jiang, Linhua ; Lacy, Mark ; McGreer, Ian D. ; Trump, Jonathan R. ; Riegel, Ryan N. / Bayesian HIGH-REDSHIFT QUASAR CLASSIFICATION from OPTICAL and MID-IR PHOTOMETRY. In: Astrophysical Journal, Supplement Series. 2015 ; Vol. 219, No. 2.
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