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The Optical Gravitational Lensing Experiment (OGLE) continuously monitorshundreds of thousands of eclipsing binaries in the field of galactic bulge andthe Magellanic Clouds. These objects have been classified into mainmorphological sub-classes, such as contact, non-contact, ellipsoidal andcataclysmic variables, both by matching the light curves with predefinedtemplates and visual inspections. Here we present the result of a machinelearned automatic classification based on the morphology of light curvesinspired by the classification of eclipsing binaries observed by the originalKepler mission. We similarly use a dimensionality reduction technique, thelocally linear embedding to map the high dimension of the data set into a lowdimensional embedding parameter space, while keeping the local geometry and thesimilarities of the neighbouring data points. After three consecutive steps, weassign one parameter to each binary star, which well scales with the"detachness", i.e. the sum of the relative radii of the components. This valueis in good agreement with the morphology types listed in the OGLE catalog and,along with the orbital periods, can be used to filter any morphologicalsub-types based on the similarity of light curves. Our open-source pipeline canbe applied in a fully automatic way to any other large data set to classifybinary stars.

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