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Gaussian-mixture-model-based cluster analysis finds five kinds of gamma-ray bursts in the BATSE catalogue
Monthly Notices of the Royal Astronomical Society
  • Souradeep Chattopadhyay, University of Calcutta
  • Ranjan Maitra, Iowa State University
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Clustering methods are an important tool to enumerate and describe the different coherent kind of gamma-ray bursts (GRBs). But their performance can be affected by a number of factors such as the choice of clustering algorithm and inherent associated assumptions, the inclusion of variables in clustering, nature of initialization methods used or the iterative algorithm or the criterion used to judge the optimal number of groups supported by the data. We analysed GRBs from the Burst and Transient Source Experiment (BATSE) 4Br Catalog using k-means and Gaussian-mixture-models-based clustering methods and found that after accounting for all the above factors, all six variables – different subsets of which have been used in the literature – that are, namely, the flux duration variables (T50, T90), the peak flux (P256) measured in 256 ms bins, the total fluence (Ft) and the spectral hardness ratios (H32 and H321) contain information on clustering. Further, our analysis found evidence of five different kinds of GRBs and that these groups have different kinds of dispersions in terms of shape, size and orientation. In terms of duration, fluence and spectrum, the five types of GRBs were characterized as intermediate/faint/intermediate, long/intermediate/soft, intermediate/intermediate/intermediate, short/faint/hard and long/bright/intermediate.


This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

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Souradeep Chattopadhyay and Ranjan Maitra. "Gaussian-mixture-model-based cluster analysis finds five kinds of gamma-ray bursts in the BATSE catalogue" Monthly Notices of the Royal Astronomical Society Vol. 469 Iss. 3 (2017) p. 3374 - 3389
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