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. (The Astrophysical journal. Supplement series, 2015)
ABSTRACT We identify 885,503 type 1 quasar candidates to 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 (of which 6779 are new photometric candidates). Our algorithm is more complete to than the traditional mid-IR selection "wedges" and to 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 . 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.