Latent characterisation of the complete BATSE gamma ray bursts catalogue using Gaussian mixture of factor analysers and model-estimated overlap-based syncytial clustering

TL;DR

This study uses Gaussian mixture of factor analysers and overlap-based clustering to analyze 1150 BATSE gamma-ray bursts, revealing a multi-layered structure with five initial groups that merge into fewer categories, enhancing understanding of GRB diversity.

Contribution

The paper introduces a novel application of Gaussian mixture of factor analysers combined with overlap-based clustering to characterize GRBs, providing a detailed multi-layered grouping approach.

Findings

Identified five ellipsoidal groups of GRBs based on nine parameters.

Merged these groups into three and then two, revealing broader categories.

Characterized groups using latent factors derived from parameters.

Abstract

Characterising and distinguishing gamma-ray bursts (GRBs) has interested astronomers for many decades. While some authors have found two or three groups of GRBs by analyzing only a few parameters, recent work identified five ellipsoidally-shaped groups upon considering nine parameters $T_{50}, T_{90}, F_1, F_2, F_3, F_4, P_{64}, P_{256}, P_{1024}$. Yet others suggest sub-classes within the two or three groups found earlier. Using a mixture model of Gaussian factor analysers, we analysed 1150 GRBs, that had nine parameters observed, from the current Burst and Transient Source Experiment (BATSE) catalogue, and again established five ellipsoidal-shaped groups to describe the GRBs. These five groups are characterised in terms of their average duration, fluence and spectrum as shorter-faint-hard, long-intermediate-soft, long-intermediate-intermediate, long-bright-intermediate and short-faint-hard. The use of factor analysers in describing individual group densities allows for a more thorough group-wise characterisation of the parameters in terms of a few latent features. However, given the discrepancy with many other existing studies that advocated for two or three groups, we also performed model-estimated overlap-based syncytial clustering (MOBSynC) that successively merges poorer-separated groups. The five ellipsoidal groups merge into three and then into two groups, one with GRBs of low durations and the other having longer duration GRBs. These groups are also characterised in terms of a few latent factors made up of the nine parameters. Our analysis provides context for all three sets of results, and in doing so, details a multi-layered characterisation of the BATSE GRBs, while also explaining the structure in their variability.