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Finding Gamma-Ray Burst Classes.

Gamma-ray bursts (GRBs) are the most energetic natural events in the universe. Traditional methods have long indicated that two distinct GRB classes exist, while newer statistical clustering techniques have demonstrated the existence of a third GRB class. We show how pattern recognition algorithms from the artificial intelligence (AI) branch of computer science can identify new GRB classes. More importantly, we use AI algorithms to explain class behaviors. In other words, AI algorithms can be used to determine whether classes are discrete source populations, or whether other effects (e.g. instrumental biases, sampling biases, data correlations, and/or intrinsic measurement errors) are responsible for producing class behaviors.
Jon Hakkila, Tim Giblin
Department of Physics and Astronomy
College of Charleston

William S. Paciesas
Department of Physics and Astronomy
University of Alabama in Huntsville

David J. Haglin, Richard J. Roiger
Department of Computer and Information Services
Minnesota State University, Mankato
COPYRIGHT 2001 South Carolina Academy of Science
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2001 Gale, Cengage Learning. All rights reserved.

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Title Annotation:using artificial intelligence algorithms
Author:Hakkila, Jon; Giblin, Tim; Paciesas, William S.; Haglin, David J.; Roiger, Richard J.
Publication:Bulletin of the South Carolina Academy of Science
Article Type:Brief Article
Geographic Code:1U5SC
Date:Jan 1, 2001
Words:147
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