arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1910.08043astro-ph.HEgr-qchep-ph
收藏

基于机器学习的新的伽马射线暴爆发前阶段

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

Yingtian Chen, Bo-Qiang Ma

摘要:

伽马射线暴(GRB)是宇宙中能量极大的爆炸事件,一般认为它包括两个阶段:瞬发阶段和随后的余辉阶段。最近的研究表明,一些高能光子在源处比瞬发相更早地发射。由于光速的变化,这些高能光子比低能光子传播得慢,因此它们在探测器上被观察到的时间比低能光子要晚。基于Fermi伽玛射线空间望远镜(FGST)观测数据,采用机器学习分类方法,详细分析了瞬发前伽玛暴的光子分布,提出了伽玛暴存在一个未知的爆发前阶段.对机器学习自动选择的光子的分析也会在$E_{\mathrm{LV}}=\mathrm{3.55\times 10^{17} GeV}$处产生光速变化。

英文摘要:

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

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑