Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry

TL;DR

This paper presents a Bayesian method to identify over 885,000 quasar candidates using combined optical and mid-IR data, significantly improving high-redshift quasar detection and catalog completeness.

Contribution

The study introduces a novel Bayesian kernel density algorithm that enhances high-redshift quasar selection beyond traditional mid-IR and optical methods.

Findings

Identified 885,503 quasar candidates including 7,874 high-z candidates.

Achieved greater completeness for z>3.5 quasars than previous methods.

Provided a large, relatively complete quasar catalog for future studies.

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 suggests 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.