Treffer: Machine learning for galaxy morphology classification
Title:
Machine learning for galaxy morphology classification
Authors:
Publisher Information:
Oxford University Press
Publication Year:
2010
Collection:
University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms:
Document Type:
Fachzeitschrift
article in journal/newspaper
Language:
English
ISSN:
00358711
Rights:
info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder
Accession Number:
edsbas.E54597F4
Database:
BASE
Weitere Informationen
In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue. ; peer-reviewed