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

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 $E_{\mathrm{LV}}=3.55\times 10^{17}\mathrm{GeV}$.

13 latex pages, 12 figures, final version for publication

References in corpus (1)

• Study of Lorentz violation in INTEGRAL Gamma-Ray Bursts

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• Probes for String-Inspired Foam, Lorentz, and CPT Violations in Astrophysics
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• New Constraints on Lorentz Invariance Violation at High Redshifts from Multiband of GRBs