Optimal Time-Series Selection of Quasars
  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.
