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首页> 外文期刊>Journal of Radiation Research: Official Organ of the Japan Radiation Research Society >Source of statistical noises in the Monte Carlo sampling techniques for coherently scattered photons
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Source of statistical noises in the Monte Carlo sampling techniques for coherently scattered photons

机译:相干散射光子的蒙特卡洛采样技术中的统计噪声源

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摘要

Detailed comparisons of the predictions of the Relativistic Form Factors (RFFs) and Modified Form Factors (MFFs) and their advantages and shortcomings in calculating elastic scattering cross sections can be found in the literature. However, the issues related to their implementation in the Monte Carlo (MC) sampling for coherently scattered photons is still under discussion. Secondly, the linear interpolation technique (LIT) is a popular method to draw the integrated values of squared RFFs/MFFs (i.e.) over squared momentum transfer. In the current study, the role/issues of RFFs/MFFs and LIT in the MC sampling for the coherent scattering were analyzed. The results showed that the relative probability density curves sampled on the basis of MFFs are unable to reveal any extra scientific information as both the RFFs and MFFs produced the same MC sampled curves. Furthermore, no relationship was established between the multiple small peaks and irregular step shapes (i.e. statistical noise) in the PDFs and either RFFs or MFFs. In fact, the noise in the PDFs appeared due to the use of LIT. The density of the noise depends upon the interval length between two consecutive points in the input data table of and has no scientific background. The probability density function curves became smoother as the interval lengths were decreased. In conclusion, these statistical noises can be efficiently removed by introducing more data points in the data tables.
机译:相对论形式因子(RFF)和修正形式因子(MFF)的预测的详细比较及其在计算弹性散射截面方面的优点和缺点可以在文献中找到。但是,与它们在相干散射光子的蒙特卡洛(MC)采样中实现有关的问题仍在讨论中。其次,线性插值技术(LIT)是一种流行的方法,用于在平方动量传递上绘制平方RFF / MFF的积分值(即)。在当前的研究中,分析了RFF / MFF和LIT在MC采样中用于相干散射的作用/问题。结果表明,基于MFF采样的相对概率密度曲线无法揭示任何额外的科学信息,因为RFF和MFF都生成相同的MC采样曲线。此外,PDF和RFF或MFF中的多个小峰和不规则台阶形状(即统计噪声)之间没有建立关系。实际上,由于使用了LIT,PDF中出现了噪音。噪声的密度取决于输入数据表中两个连续点之间的间隔长度,并且没有科学背景。随着间隔长度的减小,概率密度函数曲线变得更加平滑。总之,可以通过在数据表中引入更多数据点来有效地消除这些统计噪声。

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