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arXiv 2402.17282astro-ph.HE
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长伽马射线暴内峰数量的分布

Distribution of number of peaks within a long gamma-ray burst

C. Guidorzi, M. Sartori, R. Maccary, A. Tsvetkova, L. Amati, L. Bazzanini, M. Bulla, A. E. Camisasca, L. Ferro, F. Frontera, C. K. Li, S. L. Xiong, S. N. Zhang

AI总结:

本文首次研究长伽马射线暴光变曲线中峰数量的分布,基于四个星表数据发现其可由两个指数函数混合模型描述,揭示出峰贫乏和峰丰富两类 GRB,分别约占 80% 和 20%,对应不同的能量释放或耗散机制。

摘要:

长持续时间伽马射线暴(LGRB)光变曲线(LCs)的多样性编码了关于 LGRB 引擎在前身星坍缩后如何释放能量的丰富信息。表征 GRB 光变曲线的尝试集中于若干性质,例如最小时变时标、功率密度谱(包括系综平均和单个事件),或采用不同的变率定义。与此同时,通过研究 GRB 时间轮廓内单个峰的等待时间、峰值流量和注量的分布,人们也将其作为随机过程进行表征。然而,轮廓的多样性是否可以用一个共同的随机过程来描述,这一问题仍然悬而未决。本文首次通过研究 GRB 轮廓中峰数量的分布来解决这一问题。我们使用了四个不同的 GRB 星表:CGRO/BATSE、Swift/BAT、BeppoSAX/GRBM 和 Insight-HXMT。统计显著的峰通过经过充分测试的 MEPSA 算法识别,并进一步通过施加一组信噪比阈值进行筛选。随后我们提取了每个 GRB 对应的峰数量分布。在所考虑的不同模型(幂律、简单指数或拉伸指数)中,只有两个指数函数的混合能够拟合所有观测到的分布,这表明存在两种不同的行为:(i)每个 GRB 平均有 2.1±0.1 个峰(“峰贫乏”),约占观测到的 GRB 群体的 80%;(ii)每个 GRB 平均有 8.3±1.0 个峰(“峰丰富”),占观测群体的其余 20%。我们将峰丰富类 GRB 与亚秒级变率的存在联系起来,而这种变率在峰贫乏 GRB 中似乎不存在。这两类可能源于 GRB 引擎释放能量或能量耗散为伽马射线的两种不同机制。

英文摘要:

The variety of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information on how LGRB engines release energy following the collapse of the progenitor star. Attempts to characterise GRB LCs focused on a number of properties, such as the minimum variability timescale, power density spectra (both ensemble average and individual), or with different definitions of variability. In parallel, a characterisation as a stochastic process was pursued by studying the distributions of waiting times, peak flux, fluence of individual peaks within GRB time profiles. Yet, the question remains as to whether the diversity of profiles can be described in terms of a common stochastic process. Here we address this issue by studying for the first time the distribution of the number of peaks in a GRB profile. We used four different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and Insight-HXMT. The statistically significant peaks were identified by means of well tested algorithm MEPSA and further selected by applying a set of thresholds on signal-to-noise ratio. We then extracted the corresponding distributions of number of peaks per GRB. Among the different models considered (power-law, simple or stretched exponential) only a mixture of two exponentials models all the observed distributions, suggesting the existence of two distinct behaviours: (i) an average number of 2.1+-0.1 peaks per GRB ("peak poor") and accounting for about 80% of the observed population of GRBs; (ii) an average number of 8.3+-1.0 peaks per GRB ("peak rich") and accounting for the remaining 20% of the observed population. We associate the class of peak-rich GRBs with the presence of sub-second variability, which seems to be absent among peak-poor GRBs. The two classes could result from two different regimes through which GRB engines release energy or through which energy is dissipated into gamma-rays.

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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