Two Dimensional Classification of the Swift/BAT GRBs

Abstract

Using Gaussian Mixture Model and Expectation Maximization algorithm, we have performed a density estimation in the framework of $T_{90}$ versus hardness ratio for 296 Swift/BAT GRBs with known redshift. Here, Bayesian Information Criterion has been taken to compare different models. Our investigations show that two instead of three or more Gaussian components are favoured in both the observer and rest frames. Our key findings are consistent with some previous results.

9 pages, 8 figures, accepted by AP&SS

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Cited by in corpus (4)

• Classification of Gamma-Ray Burst durations using robust model-comparison techniques
• Testing Isotropic Universe Using the Gamma-Ray Burst Data of Fermi / GBM
• Multivariate analysis of BATSE gamma-ray burst properties using skewed distributions
• Multidimensional analysis of Fermi GBM gamma-ray bursts