S319p.240 — Quasar Classification Using Color and Variability

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Aug 10th at 6:00 PM until 6:00 PM

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Author(s): Christina M Peters1, Gordon Richards1

Institution(s): 1. Drexel University

We use the Non-parametric Bayesian Classification Kernel Density Estimation (NBC KDE) quasar selection algorithm (Richards et al. 2004) to identify 36,569 type 1 quasar candidates in the Sloan Digital Sky Survey (SDSS) Stripe 82 field using the combination of optical photometry and variability. 5-band coadded optical photometry is taken from the SDSS-I/II 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 using 10 to >100 observations for timescales from ~1 day to ~8 years. Selection was based on a training sample of 13,221 spectroscopically-confirmed type 1 quasars from SDSS-I/II and the Baryon Oscillation Spectroscopic Survey (BOSS). 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 < z < 3.5 where quasars and stars have similar optical colors. The 23,043 quasar candidates that are not spectroscopically confirmed reach a depth of coadd i ~ 22: 0 and 21,380 (92.8%) are dimmer than coadded i -band magnitude of 20.2, the cut off for spectroscopic follow-up for SDSS
DR7.