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CLASSIFYING STARS: A COMPARISON BETWEEN CLASSICAL, GENETIC AND NEURAL NETWORK ALGORITHMS

By Astronomy Large, Databases Ii, A. Heck, F. Murtagh, M. Hernandez-pajares, F. Comellas, E. Monte, J. Floris, Departament Matemàtica and Aplicada Telemàtica

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.

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.7577
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