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STARMIND: Automated Classification of Astronomical Data Based on an Hybrid Strategy

  • Alejandra Rodríguez
  • Iciar Carricajo
  • Minia Manteiga
  • Carlos Dafonte
  • Bernardino Arcay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5271)

Abstract

This paper describes the formulation and development of STARMIND, a hybrid system devoted to the automated classification of stellar spectra in the MK system. The MK system is an astronomical classification system used to cluster stars in morphological types based on stellar temperatures and luminosities. Our hybrid system is composed by a knowledge-based system that performs the first taxonomy in stellar types. A second-level system is based on Artificial Neural Networks and performs a more refined classification in stellar subtypes. Artificial Neural Networks were defined by selecting the optimal algorithms for training and architecture for each of the stellar spectra subtypes.

Keywords

Neural Networks Knowledge-based Systems Fuzzy Logic Hybrid Systems Spectral Features Classification of Stars 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alejandra Rodríguez
    • 1
  • Iciar Carricajo
    • 2
  • Minia Manteiga
    • 2
  • Carlos Dafonte
    • 1
  • Bernardino Arcay
    • 1
  1. 1.Information and Comunications Technologies Department, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Department of Navigation and Earth SciencesUniversity of A CoruñaA CoruñaSpain

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