2011ApJ...728...26M

We develop a method for separating quasars from other variable point sources using Sloan Digital Sky Survey (SDSS) Stripe 82 light-curve data for ∼ 10,000 variable objects. To statistically describe quasar variability, we use a damped random walk model parametrized by a damping timescale, τ, and an asymptotic amplitude (structure function), SF_∞. With the aid of an SDSS spectroscopically confirmed quasar sample, we demonstrate that variability selection in typical extragalactic fields with low stellar density can deliver complete samples with reasonable purity (or efficiency, E). Compared to a selection method based solely on the slope of the structure function, the inclusion of the τ information boosts E from 60% to 75% while maintaining a highly complete sample (98%) even in the absence of color information. For a completeness of C = 90%, E is boosted from 80% to 85%. Conversely, C improves from 90% to 97% while maintaining E = 80% when imposing a lower limit on τ. With the aid of color selection, the purity can be further boosted to 96%, with C = 93%. Hence, selection methods based on variability will play an important role in the selection of quasars with data provided by upcoming large sky surveys, such as Pan-STARRS and the Large Synoptic Survey Telescope (LSST). For a typical (simulated) LSST cadence over 10 years and a photometric accuracy of 0.03 mag (achieved at i ~ 22), C is expected to be 88% for a simple sample selection criterion of τ>100 days. In summary, given an adequate survey cadence, photometric variability provides an even better method than color selection for separating quasars from stars.