Abstract

The paper proposes the use of Kohonen’s Self Organizing Map (SOM), and supervised neural networks to find clusters in samples of gammaray burst (GRB) using the measurements given in BATSE GRB. The extent of separation between clusters obtained by SOM was examined by cross validation procedure using supervised neural networks for classification. A method is proposed for variable selection to reduce the “curse of dimensionality”. Six variables were chosen for cluster analysis. Additionally, principal components were computed using all the original variables and 6 components which accounted for a high percentage of variance was chosen for SOM analysis. All these methods indicate 4 or 5 clusters. Further analysis based on the average profiles of the GRB indicated a possible reduction in the number of clusters.

Highlights

  • It is of great interest to astronomers to know whether the measurements on gamma-ray burst (GRB) can be characterized by a single probability distribution around some central value or as a mixture of probability distributions around different central values

  • We consider the original BATSE 3B catalogue from the Compton Gamma Ray observatory, which is composed of 1122 GRB trigger samples with 14 measurements of astrophysical interest made on each sample

  • Our study indicates the following: a) The profile plot and the scatter plot of the first two principal components indicate a clear separation between Classes 1 and 3

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Summary

Introduction

It is of great interest to astronomers to know whether the measurements on gamma-ray burst (GRB) can be characterized by a single probability distribution around some central value or as a mixture of probability distributions around different central values. Clustering is an exploratory data analysis (EDA) for investigating such problems by looking for groups of observed samples which are well separated using a suitable criterion. The ultimate aim is to seek for a physical interpretation of differences between the groups. An interesting example in a different context is the discovery of three clusters of the general population of individuals based on some blood tests for diabetes, one identified as diabetes free, and the other two representing individuals with 2 different types of diabetes A and

15 FT sum of the four fluencies
Cluster Analysis
Cluster analysis using SOM
Cross validation
Reduction of dimensionality
Principal Component Analysis
Findings
Conclusion
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