Light curve analysis of Variable stars using Fourier decomposition and Principal component analysis

TL;DR

This study compares Fourier decomposition and principal component analysis for analyzing light curves of variable stars, demonstrating PCA's efficiency and potential for automated classification in large datasets.

Contribution

The paper introduces a comparative analysis of Fourier decomposition and PCA for variable star light curves, highlighting PCA's efficiency and its application in automated classification.

Findings

PCA reconstructs light curves with fewer components than Fourier decomposition.

PCA requires significantly less CPU time than Fourier decomposition.

PCA can classify variable stars automatically and effectively.

Abstract

Aims: We show the use of principal component analysis (PCA) and Fourier decomposition (FD) method as tools for variable star diagnostics and compare their relative performance in studying the changes in the light curve structures of pulsating Cepheids and in the classification of variable stars. Methods: We have calculated the Fourier parameters of 17,606 light curves of a variety of variables, e.g., RR Lyraes, Cepheids, Mira Variables and extrinsic variables for our analysis. We have also performed PCA on the same database of light curves. The inputs to the PCA are the 100 values of the magnitudes for each of these 17,606 light curves in the database interpolated between phase 0 to 1. Unlike some previous studies, Fourier coefficients are not used as input to the PCA. Results: We show that in general, the first few principal components (PCs) are enough to reconstruct the original light curves compared to FD method where 2 to 3 times more number of parameters are required to satisfactorily reconstruct the light curves. The computation of required number of Fourier parameters on the average needs 20 times more CPU time than the computation of required number of PCs. Therefore, PCA does have some advantage over the FD method in analysing the variable stars in a larger database. However in some cases, particularly in finding the resonances in Fundamental mode (FU) Cepheids, the PCA results show no distinct advantages over the FD method. We also demonstrate that the PCA technique can be used to classify variables into different variability classes in an automated, unsupervised way, a feature that has immense potential for larger databases of the future.