# Gaussian-Mixture-Model-based Cluster Analysis Finds Five Kinds of Gamma Ray Bursts in the BATSE Catalog

## TL;DR

This study applied Gaussian mixture models to classify Gamma Ray Bursts from the BATSE catalog, revealing five distinct groups with different spectral and temporal properties, advancing understanding of GRB diversity.

## Contribution

The paper demonstrates that Gaussian mixture models effectively identify five distinct GRB classes using multiple variables, considering various clustering factors.

## Key findings

- Identified five GRB groups with distinct spectral and temporal features.
- Showed all six variables contribute to effective clustering.
- Characterized each group by specific duration, fluence, and spectrum profiles.

## Abstract

Clustering methods are an important tool to enumerate and describe the different coherent kinds 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 analyzed GRBs from the 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 -- and that are, namely, the flux duration variables ($T_{50}$, $T_{90}$), the peak flux ($P_{256}$) measured in 256-millisecond bins, the total fluence ($F_t$) and the spectral hardness ratios ($H_{32}$ and $H_{321}$) 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.