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Classifying Stars: A Comparison Between Classical, Genetic And Neural Network Algorithms

By Hernandez-Pajares Comellas and E. Monte

Abstract

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. c fl1992 by the European Southern Observatory Astronomy from Large Databases II, Eds. A. Heck and F. Murtagh, pp. 325--330,1992, ISBN 3-923524-47-1 1 Introduction The necessity of significant improvements in the classification strategies is a fact in the 90's Astronomy. The main reason is obvious: a large and new amount of good data are being available nowadays. The main sour..

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.29.9941
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