🤖 AI Summary
This study addresses the ongoing debate regarding the optimal number of gamma-ray burst (GRB) classes—ranging from two to five—by analyzing 1,150 GRBs from the BATSE catalog using nine observational parameters. For the first time, it integrates a mixture of Gaussian factor analyzers with the Model-based Overlapping Synchronous Clustering (MOBSynC) method to identify five ellipsoidal clusters, which are subsequently hierarchically merged via MOBSynC into three and then two classes. The proposed framework reveals GRB intrinsic structure through a small set of latent factors, clearly characterizing distinct subpopulations such as “short–faint–hard” and “long–bright–intermediate.” This approach provides a unified explanation for the validity of classifications at multiple granularities and introduces the first latent-variable-driven, hierarchical model for GRB structural analysis.
📝 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 T50, T90, F1, F2, F3, F4, P64, P256, P1024. 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.