Classification of Close Binary Stars Using Recurrence Networks. (arXiv:1907.10602v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+George_S/0/1/0/all/0/1">Sandip V. George</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Misra_R/0/1/0/all/0/1">R. Misra</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ambika_G/0/1/0/all/0/1">G. Ambika</a>
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
semi-detached, 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 measures of
recurrence networks and nonlinear time series analysis to differentiate between
classes of close binary stars. We show that overcontact binary stars occupy a
region different from semi-detached and ellipsoidal binary stars in a plane of
characteristic path length(CPL) and average clustering coefficient(CC),
computed from their recurrence networks. We use standard clustering algorithms
and report that the clusters formed corresponds to the standard classes with a
high degree of accuracy.
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
semi-detached, 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 measures of
recurrence networks and nonlinear time series analysis to differentiate between
classes of close binary stars. We show that overcontact binary stars occupy a
region different from semi-detached and ellipsoidal binary stars in a plane of
characteristic path length(CPL) and average clustering coefficient(CC),
computed from their recurrence networks. We use standard clustering algorithms
and report that the clusters formed corresponds to the standard classes with a
high degree of accuracy.
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