Classifying stars: A comparison between classical, genetic and neural networks algorithms..
M. Hernández-Pajares, F.Comellas , E. Monte*, J. Floris
Departament de Matemàtica Aplicada i Telemàtica; Universitat Politècnica de Catalunya
*Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya
pp. 325--330 of Astronomy from Large Databases II; Edited by: A. Heck and F. Murtagh. European Southern Observatory, 1992. ISBN 3-923524-47-1
One of the relevant studies that are carried out with stellar samples is the segregation of stars in populations with the aid of spectral, photometric and/or kinematic data. We present the first results of the use of four different classification techniques on stellar catalogues: the Self-Organizing Map and Multi-Layer Perceptron, two different neural network architectures, and the Genetic algorithms and Hierarchical Clustering. Also the Principal Component Analysis is applied to the initial data that consist of two synthetic samples of the Solar Neighborhood with 3-D position and velocity, metallicity and age.
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