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arXiv 2406.05005astro-ph.HEgr-qc
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Fermi/GBM伽马射线暴的多样性:来自机器学习的新见解

Diversity in Fermi/GBM Gamma Ray Bursts: New insights from Machine Learning

Dimple, K. Misra, K. G. Arun

AI总结:

本文利用无监督机器学习对Fermi/GBM伽马射线暴进行分类,识别出五个与Swift/BAT结果一致的聚类,并确认两类与千新星相关的GRB,分别可能源于双中子星并合和NS-BH并合。

摘要:

伽马射线暴(GRBs)的分类一直是高能天体物理学中长期存在的难题。最近的观测挑战了传统的短暴与长暴观点,传统观点认为长暴源于大质量恒星的坍缩,而短暴源于致密双星并合。机器学习(ML)算法在解决这一问题中发挥了重要作用,揭示了Swift/BAT光变曲线数据中五个不同的GRB群组,其中两个与千新星(KNe)相关。在这项工作中,我们将分析扩展到Fermi/GBM星表,并使用无监督ML技术识别出五个聚类,与Swift/BAT的结果一致。这五个聚类在fluence-duration平面上清晰分离,暗示了fluence、duration与GRB光变曲线中的复杂性(或结构)之间可能存在联系。此外,我们确认了两类不同的与千新星相关的GRB。GRB 170817A出现在两个与千新星相关的聚类之一中,为这一类GRB可能由双中子星(BNS)并合产生的假说提供了证据。第二个与千新星相关的GRB聚类可能源自NS-BH并合。未来对致密双星在引力波(GWs)和电磁波中的多信使观测对于更好地理解这些聚类可能至关重要。

英文摘要:

Classification of gamma-ray bursts (GRBs) has been a long-standing puzzle in high-energy astrophysics. Recent observations challenge the traditional short vs. long viewpoint, where long GRBs are thought to originate from the collapse of massive stars and short GRBs from compact binary mergers. Machine learning (ML) algorithms have been instrumental in addressing this problem, revealing five distinct GRB groups within the Swift/BAT light curve data, two of which are associated with kilonovae (KNe). In this work, we extend our analysis to the Fermi/GBM catalog and identify five clusters using unsupervised ML techniques, consistent with the Swift/BAT results. These five clusters are well separated in fluence-duration plane, hinting at a potential link between fluence, duration and complexities (or structures) in the light curves of GRBs. Further, we confirm two distinct classes of KN-associated GRBs. The presence of GRB 170817A in one of the two KNe-associated clusters lends evidence to the hypothesis that this class of GRBs could potentially be produced by binary neutron star (BNS) mergers. The second KN-associated GRB cluster could potentially originate from NS-BH mergers. Future multimessenger observations of compact binaries in gravitational waves (GWs) and electromagnetic waves can be paramount in understanding these clusters better.

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。

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