Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: 100,000 z < 3 Quasars from Data Release One

We present a catalog of 100,563 unresolved, UV-excess (UVX) quasar candidates to g = 21 from 2099 deg² of the Sloan Digital Sky Survey (SDSS) Data Release One (DR1) imaging data. Existing spectra of 22,737 sources reveals that 22,191 (97.6%) are quasars; accounting for the magnitude dependence of this efficiency, we estimate that 95,502 (95.0%) of the objects in the catalog are quasars. Such a high efficiency is unprecedented in broadband surveys of quasars. This "proof-of-concept" sample is designed to be maximally efficient, but still has 94.7% completeness to unresolved, g ≲ 19.5, UVX quasars from the DR1 quasar catalog. This efficient and complete selection is the result of our application of a probability density type analysis to training sets that describe the four-dimensional color distribution of stars and spectroscopically confirmed quasars in the SDSS. Specifically, we use a nonparametric Bayesian classification, based on kernel density estimation, to parameterize the color distribution of astronomical sources—allowing for fast and robust classification. We further supplement the catalog by providing photometric redshifts and matches to FIRST/VLA, ROSAT, and USNO-B sources. Future work needed to extend this selection algorithm to larger redshifts, fainter magnitudes, and resolved sources is discussed. Finally, we examine some science applications of the catalog, particularly a tentative quasar number counts distribution covering the largest range in magnitude (14.2 < g < 21.0) ever made within the framework of a single quasar survey.