Classification of Variable Stars Using Thick-Pen Transform Method

M. Park

doi:10.1086/670671

Classification of Variable Stars Using Thick-Pen Transform Method

2013-04-01

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DOI：10.1086/670671

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Abstract

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A suitable classification of variable stars is an important task for understanding galaxy structure and evaluating stellar evolution. Most traditional approaches for classification have used various features of variable stars such as period, amplitude, color index, and Fourier coefficients. Recently, by focusing only on the light curve shape, Deb and Singh proposed a classification method based on multivariate principal component analysis (PCA). They applied the PCA method to light curves and compared its results with those obtained by Fourier coefficients. In this article, we propose a new procedure based on the thick-pen transform for obtaining accurate information on the light curve shape as well as for improving the accuracy of classification. The proposed method is applied to the data sets of variable stars from the Stellar Astrophysics and Research on Exoplanets (STARE) project and a small number of stars from Massive Compact Halo Objects (MACHO).

Keywords:

UNEQUALLY-SPACED DATA

SPECTRAL-ANALYSIS

TIME-SERIES

Classification of Variable Stars Using Thick-Pen Transform Method

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