分支过程波动的权值蒙特卡罗估计

IF 0.8 Q3 STATISTICS & PROBABILITY Monte Carlo Methods and Applications Pub Date : 2023-10-24 DOI:10.1515/mcma-2023-2015
Vladimir Uchaikin, Elena Kozhemiakina
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引用次数: 0

摘要

摘要:众所周知,使用乘法统计权值来缩短粒子轨迹建模,通常会提高程序的效率(在精度/时间比方面)。这种技巧通常用于模拟没有乘法的转移过程的非分支方案(例如,x射线辐射的转移),在这种情况下,我们只研究场特征的平均值就足够了。然而,随着能量的增加,倍增过程开始发挥重要作用(能量高于1.022 MeV的伽马量子产生电子-光子对等),此时产生的轨迹不仅仅是相空间中的断裂曲线,而是分支树。这种技术也适用于这一过程,但前提是研究统计波动和相关性不是计算的目的。本文综述了应用于粒子输运理论的蒙特卡罗方法的基本概念,证明了非分支过程中的加权方法,最后讨论了加性泛函的二阶矩和协方差的无偏估计。
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A weight Monte Carlo estimation of fluctuations in branching processes
Abstract It is well known that shortened modeling of particle trajectories with the use multiplicative statistical weights, as a rule, increases the efficiency of the program (in terms of accuracy/time ratio). This trick is often used in non-branching schemes simulating transfer processes without multiplication (for example, the transfer of X-ray radiation), in which it is sufficient to confine ourselves to studying only the average values of the field characteristics. With an increase in energy, however, multiplication processes begin to play a significant role (the production of electron-photon pairs by gamma quanta with energies above 1.022 MeV, etc.), when the resulting trajectory is not just a broken curve in the phase space, but a branched tree. This technique is also applicable to this process, but only if the study of statistical fluctuations and correlations is not the purpose of the calculation. The present review contains the basic concepts of the Monte Carlo method as applied to the theory of particle transport, demonstration of the weighting method in non-branching processes, and ends with a discussion of unbiased estimates of the second moment and covariance of additive functionals.
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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