Classification of Close Binary Stars Using Recurrence Networks
arXiv:1907.10602 · doi:10.1063/1.5120739
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 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.
arXiv comment7 pages, 8 figures, submitted to Chaos: An Interdisciplinary Journal of Nonlinear Science: Focus issue
References in corpus (15)
- The structure and function of complex networks
- From time series to complex networks: the visibility graph
- Recurrence networks - A novel paradigm for nonlinear time series analysis
- Complex Network Approach for Recurrence Analysis of Time Series
- Kepler Eclipsing Binary Stars. I. Catalog and Principal Characterization of 1879 Eclipsing Binaries in the First Data Release
- Kepler Eclipsing Binary Stars. II. 2165 Eclipsing Binaries in the Second Data Release
- Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding
- Combining complex networks and data mining: why and how
- The Geometry of Chaotic Dynamics -- A Complex Network Perspective
- A non subjective approach to the GP algorithm for analysing noisy time series
- Uniform framework for the recurrence-network analysis of chaotic time series
- The chaotic behavior of the black hole system GRS 1915+105
- Search for chaos in neutron star systems: Is Cyg X-3 a black hole?
- Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves
- Chaotic dynamics in the pulsation of DF Cygni, as observed by Kepler