Two Dimensional Classification of the Swift/BAT GRBs

TL;DR

This study applies Gaussian Mixture Models to classify Swift/BAT gamma-ray bursts based on duration and hardness ratio, finding that two components best describe the data, aligning with some prior research.

Contribution

It introduces a density estimation approach using GMM and BIC for GRB classification, favoring two components over three or more.

Findings

Two Gaussian components are preferred for classification.

Results are consistent with previous studies.

Method effectively distinguishes GRB groups.

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.