  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<z<3.5 where quasars and stars
have similar optical colors. The 22,867 quasar candidates that are not
spectroscopically confirmed reach a depth of i ~22.0; 21,876 (95.7%) are dimmer
than coadded i-band magnitude of 19.9, the cut off for spectroscopic follow-up
for SDSS on Stripe 82. Brighter than 19.9, we find 5.7% more quasar candidates
without confirming spectra in sky regions otherwise considered complete. The
resulting quasar sample has sufficient purity (and statistically correctable
incompleteness) to produce a luminosity function comparable to those determined
by spectroscopic investigations. We discuss improvements that can be made to
the process in preparation for performing similar photometric selection and
science on data from post-SDSS sky surveys.
