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A novel, completely parameter-free clustering algorithm is introduced for unsupervised classification of gamma-ray bursts (GRBs) from the BATSE catalog. The algorithm addresses the challenge of determining the optimal number of clusters, a persistent issue in GRB classification using traditional methods. Applying this algorithm recovers the established binary classification of GRBs into short and long duration bursts, aligning with the merger-collapsar theory.
A completely parameter-free clustering algorithm resolves the long-standing debate on the number of GRB classes, reinforcing the classic binary short/long burst division without parameter tuning.
Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in order to explore their physical sources. In the present scenario, the number of clusters corresponding to differentiable groups is still under conflict, in spite of numerous attempts with the state-of-the-art clustering procedures. This crucial unknown parameter needs to be evaluated, either directly or indirectly in terms of other tuning parameters, to produce the clusters in GRBs through implementation of an appropriate clustering algorithm. While most of the applied algorithms reached two physically explained groups of merger and collapsar predominated by the short and long bursts respectively, other statistical approaches violated this binary partition. However, physical establishment of any additional cluster(s) is not yet confirmed. Therefore, we propose a new algorithm, from a different stream of clustering referred to as `completely parameter-free', which carries out the classification of GRBs in a manner that has not been tried so far. It indicates two main groups, of short and long duration bursts from the BATSE sample, compatible with the merger-collapsar theory.