Abstract:
Close binary stars are binary stars where the component stars are close enough such that they can exchange mass and/or energy. They are subdivided into semidetached, overcontact, or ellipsoidal binary stars. A challenging problem in the context of close binary stars is their classification into these subclasses based solely on their light curves. Conventionally, this is done by observing subtle features in the light curves like the depths of adjacent minima, which is tedious when dealing with large datasets. In this work, we suggest the use of machine learning algorithms applied to quantifiers derived from recurrence networks to differentiate between classes of close binary stars. We show that overcontact binary stars occupy a region different from semidetached and ellipsoidal binary stars in a plane of characteristic path length and average clustering coefficient, computed from their recurrence networks. We use standard clustering algorithms and report that the clusters formed correspond to the standard classes with a high degree of accuracy. Our study aims to classify close binary stars into semidetached, overcontact, and ellipsoidal binaries based on the properties of the recurrence networks constructed from their light curves. We show how this can be automated using machine learning algorithms for faster and efficient classification. Recurrence networks have been an important addition to conventional nonlinear time series analysis tools to study the dynamical behavior of real world systems, with a major advantage in terms of the number of data points required for reliable analyses. The methods of machine learning are especially suited to deal with large numbers of astrophysical objects. In this context, the study of nonlinear dynamics of binary star systems has been mostly restricted to compact objects like neutron stars and black holes, where matter accretion leads to interesting dynamical phenomena.1,2 On the other hand, nonlinear time series analysis of noncompact binary stars has not been studied in detail. In close binary stars, the exchange of matter and energy leads to irregularities in their light curves and these irregularities reflect the variations in their underlying dynamics. We start with methods of nonlinear time series analysis to recreate the dynamics and capture the subtle variations in their dynamics by constructing recurrence networks from them. Then, clustering algorithms like k-means and support vector machines are used to see the pattern of clusters in the plane of characteristic path length (CPL) and average clustering coefficient (CC), which can correctly identify the three categories of the stars. Our method is computationally much less expensive than conventional methods and can be effective with much smaller datasets and hence an efficient way to deal with a large number of datasets