Classification of OGLE eclipsing binary stars based on their morphology type with Locally Linear Embedding

TL;DR

This paper introduces an automatic machine learning method using Locally Linear Embedding to classify OGLE eclipsing binary stars based on their light curve morphology, aligning well with existing catalog classifications.

Contribution

It presents a novel application of Locally Linear Embedding for binary star classification, enabling fully automatic, scalable analysis of large astronomical datasets.

Findings

The method accurately correlates with OGLE catalog types.

The derived parameter scales with binary star 'detachness'.

The pipeline is open-source and adaptable to other datasets.

Abstract

The Optical Gravitational Lensing Experiment (OGLE) continuously monitors hundreds of thousands of eclipsing binaries in the field of galactic bulge and the Magellanic Clouds. These objects have been classified into main morphological sub-classes, such as contact, non-contact, ellipsoidal and cataclysmic variables, both by matching the light curves with predefined templates and visual inspections. Here we present the result of a machine learned automatic classification based on the morphology of light curves inspired by the classification of eclipsing binaries observed by the original Kepler mission. We similarly use a dimensionality reduction technique, the locally linear embedding to map the high dimension of the data set into a low dimensional embedding parameter space, while keeping the local geometry and the similarities of the neighbouring data points. After three consecutive steps, we assign one parameter to each binary star, which well scales with the "detachness", i.e. the sum of the relative radii of the components. This value is in good agreement with the morphology types listed in the OGLE catalog and, along with the orbital periods, can be used to filter any morphological sub-types based on the similarity of light curves. Our open-source pipeline can be applied in a fully automatic way to any other large data set to classify binary stars.