Clustering of eclipsing binary light curves through functional principal component analysis

TL;DR

This paper introduces a novel functional principal component analysis approach for clustering eclipsing binary star light curves, improving accuracy and noise reduction over previous methods.

Contribution

It presents a new functional data transformation and PCA-based clustering method that effectively identifies true star groups with noise filtering.

Findings

Successfully distinguished two star groups with high consistency

Outperformed previous clustering results

Proved effective for large light curve datasets

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.