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
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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|
|Date:||Jan 1, 2001|
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