Quasar Probabilities and Redshifts from WISE mid-IR through GALEX UV Photometry

Abstract: Extreme deconvolution (XD) of broad-band photometric data can both separate stars from quasars and generate probability density functions for quasar redshifts, while incorporating flux uncertainties and missing data. Mid-infrared photometric colors are now widely used to identify hot dust intrinsic to quasars, and the release of all-sky WISE data has led to a dramatic increase in the number of IR-selected quasars. Using forced-photometry on public WISE data at the locations of SDSS point sources, we incorporate this all-sky data into the training of the XDQSOz models originally developed to select quasars from optical photometry. The combination of WISE and SDSS information is far more powerful than SDSS alone, particularly at $z>2$. The use of SDSS$+$WISE photometry is comparable to the use of SDSS$+$ultraviolet$+$near-IR data. We release a new public catalogue of 5,537,436 (total; 3,874,639 weighted by probability) potential quasars with probability $P_{\textrm{QSO}} > 0.2$. The catalogue includes redshift probabilities for all objects. We also release an updated version of the publicly available set of codes to calculate quasar and redshift probabilities for various combinations of data. Finally, we demonstrate that this method of selecting quasars using WISE data is both more complete and efficient than simple WISE color-cuts, especially at high redshift. Our fits verify that above $z \sim 3$ WISE colors become bluer than the standard cuts applied to select quasars. Currently, the analysis is limited to quasars with optical counterparts, and thus cannot be used to find highly obscured quasars that WISE color-cuts identify in significant numbers.