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Gamma-ray burst engines may have no memory

机译:伽马射线爆发引擎可能没有内存

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Context. A sizeable fraction of gamma-ray burst (GRB) time profiles consist of a temporal sequence of pulses. The nature of this stochastic process carries information on how GRB inner engines work. The so-called interpulse time defines the interval between adjacent pulses, excluding the long quiescence periods during which the signal drops to the background level. It was found by many authors in the past that interpulse times are lognormally distributed, at variance with the exponential case that is expected for a memoryless process. Aims. We investigated whether the simple hypothesis of a temporally uncorrelated sequence of pulses is really to be rejected, as a lognormal distribution necessarily implies. Methods. We selected and analysed a number of multi-peaked CGRO/BATSE GRBs and simulated similar time profiles, with the crucial difference that we assumed exponentially distributed interpulse times, as is expected for a memoryless stationary Poisson process. We then identified peaks in both data sets using a novel peak search algorithm, which is more efficient than others used in the past. Results. We independently confirmed that the observed interpulse time distribution is approximately lognormal. However, we found the same results on the simulated profiles, in spite of the intrinsic exponential distribution. Although intrinsic lognormality cannot be ruled out, this shows that intrinsic interpulse time distribution in real data could still be exponential, while the observed lognormal could be ascribed to the low efficiency of peak search algorithms at short values combined with the limitations of a bin-integrated profile. Conclusions. Our result suggests that GRB engines may emit pulses after the fashion of nuclear radioactive decay, that is, as a memoryless process.
机译:上下文。相当一部分的伽马射线爆发(GRB)时间分布图由脉冲的时间序列组成。这种随机过程的性质包含有关GRB内部引擎如何工作的信息。所谓的脉冲间时间定义了相邻脉冲之间的间隔,不包括信号下降到背景电平的较长的静态周期。过去许多作者发现,脉冲时间是对数正态分布的,与无记忆过程所期望的指数情况不同。目的我们调查了时间不相关的脉冲序列的简单假设是否真的要被拒绝,因为对数正态分布必然意味着。方法。我们选择并分析了许多多峰CGRO / BATSE GRB,并模拟了类似的时间曲线,其中关键的差异是我们假设指数分布的脉冲时间,这是无记忆平稳泊松过程所期望的。然后,我们使用新颖的峰搜索算法在两个数据集中确定了峰,该算法比过去使用的其他算法更有效。结果。我们独立地确认观察到的脉冲时间分布近似为对数正态。但是,尽管具有内在指数分布,我们仍在模拟轮廓上发现了相同的结果。尽管不能排除内在对数正态性,但这表明真实数据中的内在脉冲间时间分布仍然可以是指数的,而观察到的对数正态可归因于峰值搜索算法在短值时效率低以及bin积分的局限性个人资料。结论。我们的结果表明,GRB引擎可能会以核放射性衰变的方式发出脉冲,即无记忆过程。

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