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The EPOCH (EROS-2 periodic variable star classification using machine learning) project
aims to detect periodic variable stars in the EROS-2 light curve database. In this paper,
we present the first 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
from 28 392 light curves of the corresponding EROS-2 sources. We then used the random
forest method to classify the EROS-2 sources in the training set. We designed the model to
separate not only δ Scuti stars, RR Lyraes, Cepheids, eclipsing
binaries, and long-period variables, the superclasses, but also their subclasses, such as
RRab, RRc, RRd, and RRe for RR Lyraes, and similarly for the other variable types. The
model trained using only the superclasses shows 99% recall and precision, while the model
trained on all subclasses shows 87% recall and precision. 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, 6607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and
11 394 long-period variables
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