Title: Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field,
Abstract: Statistical pattern recognition methods have provided competitive solutions
for variable star classification at a relatively low computational cost. In
order to perform supervised classification, a set of features is proposed and
used to train an automatic classification system. Quantities related to the
magnitude density of the light curves and their Fourier coefficients have been
chosen as features in previous studies. However, some of these features are not
robust to the presence of outliers and the calculation of Fourier coefficients
is computationally expensive for large data sets. We propose and evaluate the
performance of a new robust set of features using supervised classifiers in
order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic
pole field. We calculated the proposed set of features on six types of variable
stars and on a set of Be star candidates reported in the literature. We
evaluated the performance of these features using classification trees and
random forests along with K-nearest neighbours, support vector machines, and
gradient boosted trees methods. We tuned the classifiers with a 10-fold
cross-validation and grid search. We validated the performance of the best
classifier on a set of OGLE-IV light curves and applied this to find new Be
star candidates. The random forest classifier outperformed the others. By using
the random forest classifier and colour criteria we found 50 Be star candidates
in the direction of the Gaia south ecliptic pole field, four of which have
infrared colours consistent with Herbig Ae/Be stars. Supervised methods are
very useful in order to obtain preliminary samples of variable stars extracted
from large databases. As usual, the stars classified as Be stars candidates
must be checked for the colours and spectroscopic characteristics expected for
them.