SIMBAD references

2024MNRAS.532.1434Z - Mon. Not. R. Astron. Soc., 532, 1434-1443 (2024/August-1)

Classification of Fermi gamma-ray bursts based on machine learning.

ZHU S.-Y., SUN W.-P., MA D.-L. and ZHANG F.-W.

Abstract (from CDS):

Gamma-ray bursts (GRBs) are typically classified into long and short GRBs based on their durations. However, there is a significant overlapping in the duration distributions of these two categories. In this paper, we apply the unsupervised dimensionality reduction algorithm called t-SNE and UMAP to classify 2061 Fermi GRBs based on four observed quantities: duration, peak energy, fluence, and peak flux. The map results of t-SNE and UMAP show a clear division of these GRBs into two clusters. We mark the two clusters as GRBs-I and GRBs-II, and find that all GRBs associated with supernovae are classified as GRBs-II. It includes the peculiar short GRB 200826A, which was confirmed to originate from the death of a massive star. Furthermore, except for two extreme events GRB 211211A and GRB 230307A, all GRBs associated with kilonovae fall into GRBs-I population. By comparing to the traditional classification of short and long GRBs, the distribution of durations for GRBs-I and GRBs-II do not have a fixed boundary. We find that more than 10 per cent of GRBs-I have a duration greater than 2 s, while approximately 1 per cent of GRBs-II have a duration shorter than 2 s.

Abstract Copyright: © 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

Journal keyword(s): transients: gamma-ray bursts - methods: statistical

Status at CDS : Large table(s) will be appraised for possible ingestion in VizieR.

Simbad objects: 49

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