{ "id": "1508.04121", "version": "v1", "published": "2015-08-17T19:37:46.000Z", "updated": "2015-08-17T19:37:46.000Z", "title": "Quasar Classification Using Color and Variability", "authors": [ "Christina M. Peters", "Gordon T. Richards", "Adam D. Myers", "Michael A. Strauss", "Kasper B. Schmidt", "Željko Ivezić", "Nicholas P. Ross", "Chelsea L. MacLeod", "Ryan Riegel" ], "comment": "32 pages, 23 figures. Accepted for publication in ApJS. Data file is available at http://oberon.physics.drexel.edu/~tinapeters/quasarclassification/Peters2015Catalog_30042015.fit.bz2", "categories": [ "astro-ph.GA" ], "abstract": "We conduct a pilot investigation to determine the optimal combination of color and variability information to identify quasars in current and future multi-epoch optical surveys. We use a Bayesian quasar selection algorithm (Richards et al. 2004) to identify 35,820 type 1 quasar candidates in a 239 square degree field of the Sloan Digital Sky Survey (SDSS) Stripe 82, using a combination of optical photometry and variability. Color analysis is performed on 5-band single- and multi-epoch SDSS optical photometry to a depth of r ~22.4. From these data, variability parameters are calculated by fitting the structure function of each object in each band with a power law model using 10 to >100 observations over timescales from ~1 day to ~8 years. Selection was based on a training sample of 13,221 spectroscopically-confirmed type-1 quasars, largely from the SDSS. Using variability alone, colors alone, and combining variability and colors we achieve 91%, 93%, and 97% quasar completeness and 98%, 98%, and 97% efficiency respectively, with particular improvement in the selection of quasars at 2.7