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>AN OPTIMIZED METHOD TO IDENTIFY RR Lyrae STARS IN THE SDSS×Pan-STARRS1 OVERLAPPING AREA USING A BAYESIAN GENERATIVE TECHNIQUE
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AN OPTIMIZED METHOD TO IDENTIFY RR Lyrae STARS IN THE SDSS×Pan-STARRS1 OVERLAPPING AREA USING A BAYESIAN GENERATIVE TECHNIQUE
We present a method for selecting RR Lyrae (RRL) stars (or other types 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 8115 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 3π 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×PS1 footprint, the technique presented here would work well on any multi-band, multi-epoch survey for which the number of epochs is limited.
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机译：我们提出了一种在没有大量多历元数据和光曲线分析的情况下选择RR天琴星（RRL）星（或其他类型的可变星）的方法。我们的方法使用颜色和可变性选择切割，这些切割是通过对636个预先确定的RRL星应用高斯混合贝叶斯生成方法（GMM）而不是通常使用的矩形切割来定义的。具体来说，我们的方法使用Sloan Digital Sky Survey（SDSS）的GMM色彩剪切和Panoramic Survey Telescope和Rapid Response System 13π测量（PS1）的GMM可变性剪切来选择8115个RRL候选对象（心轴距离<？70？kpc）。将我们的方法与RRL恒星的Stripe 82目录进行比较表明，该方法的效率和完整性等级分别为〜77％和〜52％。大多数污染物要么是恒定恒星，要么是日食系统中的恒星。这里描述的方法有效地恢复了已知的恒星晕子结构。可以预期，随着调查结束之前将观察到额外的PS1时期（每个过滤器约3个时期），当前的完整性和效率水平将进一步提高。使用GMM方法将我们的效率和完整性水平与通常使用的矩形切口的效率和完整性水平进行比较，可以将效率水平从〜13％显着提高到〜77％，并且完整性水平的变化不明显。因此，我们赞成在以后的研究中使用GMM技术。尽管我们在SDSS×PS1占位面积上进行了开发，但此处介绍的技术适用于任何时期数有限的多波段，多时期调查。
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