OPTIMAL TIME-SERIES SELECTION OF QUASARS

Abstract

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al. (2009), we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics --- one describing the fit confidence and one describing the probability of a false alarm --- which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as <3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5<z<3, where selection by color is extremeley inefficient. This represents 1875 new quasars in a 290 deg^2 field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discus the utility of the method at high-redshift and in the regime of noisy and sparse data. Our time series selection complements well independent selection based on quasar colors and has strong potential for identifying high redshift quasars for BAO and other cosmology studies in the LSST era.