Abstract
In order to perform a supervised classification of variable stars, we propose and evaluate a set of six features extracted from the magnitude density of the light curves. They are used to train automatic classification systems using state-of-the-art classifiers implemented in the R statistical computing environment. We find that random forests is the most successful method to select variables.
Highlights
Machine learning techniques have proved to be quite useful in classification of variable stars
Quantities related to the magnitude density of the light curves and their Fourier coefficients are chosen as features
In order to perform a supervised classification, we propose and evaluate a set of six robust descriptive statistics that can be calculated efficiently and do not need to be checked externally. We calculate this set of features for OGLE-III variables belonging to the Milky Way and the LMC and SMC galaxies, classified as Cepheids (Ceph), δ Scuti (δ-Sct), Eclipsing Binaries (EBS), Long Period Variables (LPV), RR Lyræ (RRLyr), Type 2 Cepheids (T2Ceph) and a set of Be Star Candidates (BeSC) reported in the literature
Summary
Machine learning techniques have proved to be quite useful in classification of variable stars. Quantities related to the magnitude density of the light curves and their Fourier coefficients are chosen as features. The calculation of Fourier coefficients is computationally expensive for large data sets.
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