Morphology of OGLE LCs of Eclipsing binaries

These objects have been classified into major morphological subclasses, such as contact, noncontact, ellipsoidal, and cataclysmic variables, both by matching the light curves with predefined templates and by 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 with 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 neighboring data points. After three consecutive steps, we assign one parameter to each binary star, which scales well with the "detachness," i.e., the sum of the relative radii of the components.