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We present a method for selecting RR Lyrae (RRL) stars (or other type ofvariable stars) in the absence of a large number of multi-epoch data and lightcurve analyses. Our method uses color and variability selection cuts that aredefined by applying a Gaussian Mixture Bayesian Generative Method (GMM) on 636pre-identified RRL stars instead of applying the commonly used rectangularcuts. Specifically, our method selects 8,115 RRL candidates (heliocentricdistances < 70 kpc) using GMM color cuts from the Sloan Digital Sky Survey(SDSS) and GMM variability cuts from the Panoramic Survey Telescope and RapidResponse System 1 3pi survey (PS1). Comparing our method with the Stripe 82catalog of RRL stars shows that the efficiency and completeness levels of ourmethod are ~77% and ~52%, respectively. Most contaminants are eithernon-variable main-sequence stars or stars in eclipsing systems. The methoddescribed here efficiently recovers known stellar halo substructures. It isexpected that the current completeness and efficiency levels will furtherimprove with the additional PS1 epochs (~3 epochs per filter) that will beobserved before the conclusion of the survey. A comparison between ourefficiency and completeness levels using the GMM method to the efficiency andcompleteness levels using rectangular cuts that are commonly used yielded asignificant increase in the efficiency level from ~13% to ~77% and aninsignificant change in the completeness levels. Hence, we favor using the GMMtechnique in future studies. Although we develop it over the SDSS X PS1footprint, the technique presented here would work well on any multi-band,multi-epoch survey for which the number of epochs is limited.

An optimized Method to Identify RR Lyrae stars in the SDSS X Pan-STARRS1 Overlapping Area Using a Bayesian Generative Technique