Functional Data Classification of Variable Stars

  • Park, Minjeong (Statistical Research Institute, Statistics Korea) ;
  • Kim, Donghoh (Department of Applied Mathematics, Sejong University) ;
  • Cho, Sinsup (Department of Statistics, Seoul National University) ;
  • Oh, Hee-Seok (Department of Statistics, Seoul National University)
  • Received : 2013.02.27
  • Accepted : 2013.07.12
  • Published : 2013.07.31


This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).


Supported by : Rural Development Administration, National Research Foundation of Korea(NRF)


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