📝 SummarXiv

Abstract

Gamma-ray bursts (GRBs) are generally believed to originate from two distinct progenitors, compact binary mergers and massive collapsars. Traditional and some recent machine learning-based classification schemes predominantly rely on observer-frame physical parameters, which are significantly affected by the redshift effects and may not accurately represent the intrinsic properties of GRBs. In particular, the progenitors usually could only be decided by successful detection of the multi-band long-term afterglow, which could easily cost days of devoted effort from multiple global observational utilities. In this work, we apply the unsupervised machine learning (ML) algorithms called t-SNE and UMAP to perform GRB classification based on rest-frame prompt emission parameters. The map results of both t-SNE and UMAP reveal a clear division of these GRBs into two clusters, denoted as GRBs-I and GRBs-II. We find that all supernova-associated GRBs, including the atypical short-duration burst GRB 200826A (now recognized as collapsar-origin), consistently fall within the GRBs-II category. Conversely, all kilonova-associated GRBs (except for two controversial events) are classified as GRBs-I, including the peculiar long-duration burst GRB 060614 originating from a merger event. In another words, this clear ML separation of two types of GRBs based only on prompt properties could correctly predict the results of progenitors without follow-up afterglow properties. Comparative analysis with conventional classification methods using $T_{90}$ and $E_{\rm p,z}$--$E_{\rm iso}$ correlation demonstrates that our machine learning approach provides superior discriminative power, particularly in resolving ambiguous cases of hybrid GRBs.