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

Authors:
Alejandra Rodríguez;Iciar Carricajo;Minia Manteiga;Carlos Dafonte;Bernardino Arcay
Affiliations:
Information and Comunications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain 15071;Department of Navigation and Earth Sciences, University of A Coruña, A Coruña, Spain 15011;Department of Navigation and Earth Sciences, University of A Coruña, A Coruña, Spain 15011;Information and Comunications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain 15071;Information and Comunications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain 15071
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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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.