Using Gaussian Mixture Model and Expectation Maximization algorithm, we have performed a density estimation in the framework of T90 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.
@article{arxiv.1606.01468,
title = {Two Dimensional Classification of the Swift/BAT GRBs},
author = {E. B. Yang and Z. B. Zhang and X. X. Jiang},
journal= {arXiv preprint arXiv:1606.01468},
year = {2016}
}