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arXiv 1507.04886astro-ph.HEastro-ph.COstat.ML
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区分短和长$Fermi$伽马射线暴

Distinguishing short and long $Fermi$ gamma-ray bursts

Mariusz Tarnopolski

摘要:

两类伽马射线暴(GRB),短的和长的,已经确定没有任何疑问,通常归因于不同的祖先,但这些类重叠的各种描述参数。一个子样本的46个长和22个短的$Fermi$伽玛暴估计赫斯特指数(HE),补充最小变异时间尺度(MVTS)和持续时间($T_{90}$)是用来执行监督机器学习(ML)和蒙特卡罗(MC)模拟使用支持向量机(SVM)算法。结果发现,虽然$T_{90}$本身在区分短和长GRB方面表现得很好,但当训练集由MVTS和HE补充时,整体成功率更高。这些结果可能允许引入一个新的(非线性)参数,可能会提供更模糊的分类伽玛暴。

英文摘要:

Two classes of gamma-ray bursts (GRBs), short and long, have been determined without any doubts, and are usually ascribed to different progenitors, yet these classes overlap for a variety of descriptive parameters. A subsample of 46 long and 22 short $Fermi$ GRBs with estimated Hurst Exponents (HEs), complemented by minimum variability time-scales (MVTS) and durations ($T_{90}$) is used to perform a supervised Machine Learning (ML) and Monte Carlo (MC) simulation using a Support Vector Machine (SVM) algorithm. It is found that while $T_{90}$ itself performs very well in distinguishing short and long GRBs, the overall success ratio is higher when the training set is complemented by MVTS and HE. These results may allow to introduce a new (non-linear) parameter that might provide less ambiguous classification of GRBs.

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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