EPOCH (EROS-2 periodic variable star classification using machine learning)

Variability features used in this study

Variability features used in this study

TheEPOCH project aims to detect periodic variable stars in the EROS-2 light-curve database. In the first work (Kim et al. 2014) of its series, we present the result of the classification of periodic variable stars in the EROS-2 LMC database. To classify these variables, we first built a training set by compiling known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. We crossmatched these variables with the EROS-2 sources and extracted 22 variability features (see Table 1, Kim et al. 2011, and Kim et al. 2012) from 28 392 light curves of the corresponding EROS-2 sources.

Figure 1. Feature importance estimated by Random Forest

Figure 1. Feature importance estimated by Random Forest

We applied the trained model to the entire EROS-2 LMC database, which contains about 29 million sources, and found 117 234 periodic variable candidates. Out of these 117 234 periodic variables, 55 285 have not been discovered by either OGLE or MACHO variability studies. This set comprises 1906 . δ Scuti stars, 6 607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and 11 394 long-period variables. A catalog of these EROS-2 LMC periodic variable stars is available online at http://stardb.yonsei.ac.kr and at the CDS website.