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2 March 1994 Taxonomy of gamma ray burster data using a self-organizing neural network
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Abstract
This paper summarizes the results of a signal taxonomy study of gamma ray burst (GRB) data acquired with sensors on-board the Pioneer-Venus Orbiter (PVO) spacecraft. GRB events produce large fluxes of gamma rays with durations of seconds to minutes and have been observed since the early 1970's. The true nature of GRB's is still unknown, and several competing theories exist. A fundamental point of contention among such theories is whether or not different types of GRB exist. If different types of GRB's are discovered in the existing PVO data base, the differences may correlate with their position or source characteristics. Hence, the goal of this project was to use artificial neural networks to perform signal taxonomy on the GRB data base to determine if unique classes or types of GRB's exist. A total of 26 signal features were identified, some of which can be associated directly with some characteristic of the GRB, such as duration, peak count rate, and gamma ray spectrum hardness. Additional features that were selected included the number of zero crossings in the wavelet transform and the fractal dimension of each signal. A self organizing neural network was used with the signal features to search for correlations among the signals contained in the database. The results of this analysis revealed an intrinsic dimensionality of 2 or 3 in the database. That is, it appears as though 2 or 3 distinct types of GRB may exist. In particular, two of the classes contain roughly 90% of the signals in the database of GRB signals we had to work with. These two classes are similar in characteristics but are still sufficiently distinct from one another to form separate categories. The third class of GRB is definitely distinct from the first two.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chris W. Baumgart "Taxonomy of gamma ray burster data using a self-organizing neural network", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170004
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