Eight-Dimensional Mid-Infrared/Optical Bayesian Quasar Selection
  We explore the multidimensional, multiwavelength selection of quasars from mid-IR (MIR) plus optical data, specifically from Spitzer-IRAC and the Sloan Digital Sky Survey (SDSS). We apply modern statistical techniques to combined Spitzer MIR and SDSS optical data, allowing up to 8-D color selection of quasars. Using a Bayesian selection method, we catalog 5546 quasar candidates to an 8.0 um depth of 56 uJy over an area of ~24 sq. deg; ~70% of these candidates are not identified by applying the same Bayesian algorithm to 4-color SDSS optical data alone. Our selection recovers 97.7% of known type 1 quasars in this area and greatly improves the effectiveness of identifying 3.5<z<5 quasars. Even using only the two shortest wavelength IRAC bandpasses, it is possible to use our Bayesian techniques to select quasars with 97% completeness and as little as 10% contamination. This sample has a photometric redshift accuracy of 93.6% (Delta Z +/-0.3), remaining roughly constant when the two reddest MIR bands are excluded. While our methods are designed to find type 1 (unobscured) quasars, as many as 1200 of the objects are type 2 (obscured) quasar candidates. Coupling deep optical imaging data with deep mid-IR data could enable selection of quasars in significant numbers past the peak of the quasar luminosity function (QLF) to at least z~4. Such a sample would constrain the shape of the QLF and enable quasar clustering studies over the largest range of redshift and luminosity to date, yielding significant gains in our understanding of quasars and the evolution of galaxies. 
Gordon T. Richards (1), Rajesh P. Deo (1), Mark Lacy (2), Adam D.   Myers (3), Robert C. Nichol (4), Nadia L. Zakamska (5), ...