An optimized Method to Identify RR Lyrae stars in the SDSS X Pan-STARRS1
  Overlapping Area Using a Bayesian Generative Technique
  We present a method for selecting RR Lyrae (RRL) stars (or other type of
variable stars) in the absence of a large number of multi-epoch data and light
curve analyses. Our method uses color and variability selection cuts that are
defined by applying a Gaussian Mixture Bayesian Generative Method (GMM) on 636
pre-identified RRL stars instead of applying the commonly used rectangular
cuts. Specifically, our method selects 8,115 RRL candidates (heliocentric
distances < 70 kpc) using GMM color cuts from the Sloan Digital Sky Survey
(SDSS) and GMM variability cuts from the Panoramic Survey Telescope and Rapid
Response System 1 3pi survey (PS1). Comparing our method with the Stripe 82
catalog of RRL stars shows that the efficiency and completeness levels of our
method are ~77% and ~52%, respectively. Most contaminants are either
non-variable main-sequence stars or stars in eclipsing systems. The method
described here efficiently recovers known stellar halo substructures. It is
expected that the current completeness and efficiency levels will further
improve with the additional PS1 epochs (~3 epochs per filter) that will be
observed before the conclusion of the survey. A comparison between our
efficiency and completeness levels using the GMM method to the efficiency and
completeness levels using rectangular cuts that are commonly used yielded a
significant increase in the efficiency level from ~13% to ~77% and an
insignificant change in the completeness levels. Hence, we favor using the GMM
technique in future studies. Although we develop it over the SDSS X PS1
footprint, the technique presented here would work well on any multi-band,
multi-epoch survey for which the number of epochs is limited.
