TL;DR
This paper introduces a machine learning package capable of classifying periodic variable stars from optical light curves with high accuracy, regardless of survey specifics, using features like period and skewness.
Contribution
It presents a survey-independent classifier trained on 16 features, achieving high precision and recall, and provides an open-source software tool for the astronomical community.
Findings
Achieves 0.98 precision and recall for superclasses.
Subclass classification recall/precision: 0.92/0.98 on MACHO data.
Performance remains stable with over 80 data points and weeks of observations.
Abstract
We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision…
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