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

TL;DR

This paper introduces a Bayesian Gaussian Mixture Model approach to identify RR Lyrae stars in large sky surveys, significantly improving efficiency over traditional methods and applicable to limited-epoch multi-band data.

Contribution

The paper presents a novel GMM-based method for selecting RR Lyrae stars that outperforms rectangular cuts in efficiency and is adaptable to surveys with few epochs.

Findings

Efficiency increased from ~13% to ~77% using GMM.

Method recovers known stellar halo substructures effectively.

Performance expected to improve with additional survey epochs.

Abstract

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.