Novel pre-burst stage of gamma-ray bursts from machine learning

TL;DR

This paper uses machine learning to analyze Fermi Gamma-ray Space Telescope data, revealing a previously unknown pre-burst stage of gamma-ray bursts and evidence of light speed variation at high energies.

Contribution

It introduces a novel machine learning classification method to identify a pre-burst stage in GRBs, expanding understanding of their emission timeline.

Findings

Identification of a pre-burst stage in GRBs.

Evidence of light speed variation at $E_{LV}=3.55\times 10^{17}$ GeV.

Enhanced analysis of photon data before the prompt emission.

Abstract

Gamma-ray bursts (GRBs), as extremely energetic explosions in the universe, are widely believed to consist of two stages: the prompt phase and the subsequent afterglow. Recent studies indicate that some high-energy photons are emitted earlier at source than the prompt phase. Due to the light speed variation, these high-energy photons travel slowly than the low-energy photons, so that they are observed after the prompt low-energy photons at the detector. Based on the data from the Fermi Gamma-ray Space Telescope (FGST), we analyse the photon distribution before the prompt emission in detail and propose the existence of a hitherto unknown pre-burst stage of GRBs by adopting a classification method of machine learning. Analysis on the photons automatically selected by machine learning also produce a light speed variation at $E_{\mathrm{LV}}=\mathrm{3.55\times 10^{17} GeV}$.