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 1 906 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
will be available online at http://stardb.yonsei.ac.kr and at the CDS website
(http://vizier.u-strasbg.fr/viz-bin/VizieR).Comment: 18 pages, 20 figures, suggseted language-editing by the A&A editorial
office is applie