Graph-based clustering of gamma-ray bursts

TL;DR

This paper explores graph-based clustering methods to classify gamma-ray bursts, finding that two or three groups are plausible but no definitive evidence supports more than two classes.

Contribution

It applies graph theory-based clustering techniques to GRB data, providing a semi-supervised approach to classify bursts in the duration-hardness space.

Findings

Two or three clusters are feasible for GRB classification.

No clear evidence supports more than three classes.

Sample size affects the ambiguity of clustering results.

Abstract

Aims. An attempt to classify gamma-ray bursts (GRBs) with a low level of supervision using the state-of-the-start approaches stemming from graph theory was undertaken. Methods. Graph-based classification methods, relying on different variants of the $k$-nearest neighbour graph, were applied to various GRB samples in the duration-hardness ratio parameter space to infer the optimal partitioning. Results. In most cases it is found that both two and three groups are feasible, with the outcome being more ambiguous with an increasing sample size. Conclusions. There is no clear indication of the presence of a third GRB class; however, such a possibility cannot be ruled out with the employed methodology. There are no hints at more than three classes though.