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arXiv · 1507.07788

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
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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 3.5 than the traditional mid-IR selection "wedges" and to 2.2 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.

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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. 2015-07-28. Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry. https://doi.org/10.1088/0067-0049%2F219%2F2%2F39

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