Clustering of eclipsing binary light curves through functional principal component analysis

Abstract

In this paper, we revisit the problem of clustering 1318 new variable stars found in the Milky way. Our recent work distinguishes these stars based on their light curves which are univariate series of brightness from the stars observed at discrete time points. This work proposes a new approach to look at these discrete series as continuous curves over time by transforming them into functional data. Then, functional principal component analysis is performed using these functional light curves. Clustering based on the significant functional principal components reveals two distinct groups of eclipsing binaries with consistency and superiority compared to our previous results. This method is established as a new powerful light curve-based classifier, where implementation of a simple clustering algorithm is effective enough to uncover the true clusters based merely on the first few relevant functional principal components. Simultaneously we discard the noise from the data study involving the higher order functional principal components. Thus the suggested method is very useful for clustering big light curve data sets which is also verified by our simulation study.

arXiv comment32 pages, 12 figures; Accepted for publication in the journal 'Astrophysics & Space Science'

References in corpus (14)

• Kepler Eclipsing Binary Stars. VII. The Catalog of Eclipsing Binaries Found in the Entire Kepler Data-Set
• Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding
• Light curve analysis of Variable stars using Fourier decomposition and Principal component analysis
• Unsupervised classification of eclipsing binary light curves through k-medoids clustering
• Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
• A new measure for assessment of clustering based on kernel density estimation
• Distinction of groups of gamma-ray bursts in the BATSE catalog through fuzzy clustering
• Clustering of Gamma-Ray bursts through kernel principal component analysis
• Two phase formation of massive elliptical galaxies : study through cross-correlation including spatial effect
• A new nonparametric test for two sample multivariate location problem with application to astronomy
• Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia
• Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps
• A new nonparametric interpoint distance-based measure for assessment of clustering
• 1318 New Variable Stars in a 0.25 Square Degree Region of the Galactic Plane

Cited by in corpus (5)

• A new measure for assessment of clustering based on kernel density estimation
• A new nonparametric interpoint distance-based measure for assessment of clustering
• A new interpoint distance-based clustering algorithm using kernel density estimation
• Evaluation of the number of clusters in a data set using $p$-values from Multiple Tests of Hypotheses
• Confirmation of Binary Clustering in Gamma-Ray Bursts through an Integrated $p$-value from Multiple Nonparametric Tests of Hypotheses