Improved Variable Star Search in Large Photometric Data Sets -- New Variables in CoRoT Field LRa02 Detected by BEST II

TL;DR

This study enhances the detection of variable stars in large photometric datasets by optimizing variability search methods, leading to nearly doubling the number of identified variables in the CoRoT LRa02 field.

Contribution

The paper introduces an empirical approach using analysis of variance to improve variability detection, significantly reducing false positives and increasing discovered variables.

Findings

Nearly doubled the number of detected variable stars.

Presented a catalog of 272 new periodic variables and 52 suspected variables.

Improved ephemerides for 19 known variables.

Abstract

The CoRoT field LRa02 has been observed with the Berlin Exoplanet Search Telescope II (BEST II) during the southern summer 2007/2008. A first analysis of stellar variability led to the publication of 345 newly discovered variable stars. Now, a deeper analysis of this data set was used to optimize the variability search procedure. Several methods and parameters have been tested in order to improve the selection process compared to the widely used J index for variability ranking. This paper describes an empirical approach to treat systematic trends in photometric data based upon the analysis of variance statistics that can significantly decrease the rate of false detections. Finally, the process of reanalysis and method improvement has virtually doubled the number of variable stars compared to the first analysis by Kabath et al. A supplementary catalog of 272 previously unknown periodic variables plus 52 stars with suspected variability is presented. Improved ephemerides are given for 19 known variables in the field. In addition, the BEST II results are compared with CoRoT data and its automatic variability classification.