粒子(E≥45 MeV)核内级联Monte Carlo模拟程序研究

本文基于核内级联物理过程,采用Monte Carlo方法发展了一款质子、中子以及π介子的粒子输运程序.基本物理模型基于适当简化和核内级联Bertini模型,同时借鉴了INCL模型质心系下的角微分分布以克服Bertini模型之不足,即采用Monte Carlo方法模拟核子与核子、核子与π介子间的弹性散射、非弹性散射等过程,粒子相互作用时,核子密度随半径变化且作用截面参考Bertini模型22类实验截面数据,出射粒子散射角在质心系下的抽样遵从INCL模型所确定的微分分布.可模拟45—3500 MeV的中子、质子或2500 MeV以下π介子引起的核内级联过程.入射粒子能量在60—378 MeV范围内反应截面理论计算值与已有实验数据、以及在65—3000 MeV较宽能区范围内反应截面、出射粒子增殖比、微分截面和剩余核等计算结果与MCNPX,GEANT 4和PHITS模拟结果符合较好.     

Monte Carlo方法在单粒子翻转数值模拟中的应用

描述了在单粒子翻转数值模拟中的一种有效方法-Monte Carlo方法,并对该方法中的粒子输运过程和相关的随机抽样进行了描述.对14MeV的中子从存储器硅片表面随机入射引起的单粒子翻转进行了计算和分析,同时计算了Monte Carlo方法引起的误差.     

基于Boltzmann输运方程的SPECT系统解析建模方法

在单光子发射断层成像(SPECT)中, 为了校正劣化因素的影响, 提高图像质量, 需要对SPECT成像的物理过程进行准确建模. 本文提出了基于Boltzmann输运方程及其Neumann级数解理论的SPECT系统解析建模方法, 并采用数论高维数值积分算法对解析建模公式进行数值求解. 分别对点源、均匀圆柱体模型和NCAT模型进行SPECT投影过程计算, 将其结果与传统的Monte Carlo建模方法进行比较. 结果表明解析建模方法的计算速度和精度综合性能优于Monte Carlo建模方法, 且具有不受统计噪声影响的优点, 因而更适于进行SPECT成像过程的建模.     

10—20MeV中子引起单粒子翻转能量沉积统计分析

基于Monte Carlo计算模拟, 对10—20MeV中子引起存储器单粒子翻转能量沉积进行了统计分析, 为了解单粒子翻转随机过程提供能量沉积统计信息.     

静态随机存储器单粒子翻转的Monte Carlo模拟

利用蒙特卡罗(Monte Carlo)方法，对10—20MeV 中子在静态随机存储器(SRAM)中引起的单粒子翻转进行了模拟，着重对中子在SRAM 灵敏区引起的电离能量沉积进行了计算，并对中子引起单粒子翻转过程相关物理量进行了计算.这些计算模拟结果为了解10—20MeV 中子引起SRAM 单粒子翻转过程提供了详细的统计信息，为SRAM 的抗辐射加固提供相关参考信息. 关键词： SRAM单粒子翻转 Monte Carlo 模拟 能量沉积     

低能电子在固体表面背散射系数的直接Monte Carlo方法模拟

应用单次碰撞的直接Monte Carlo方法计算能量范围从100 eV~10 keV的电子在固体Al,Si,Au表面的背散射系数,其中低能电子在固体中的弹性散射和非弹性散射截面分别应用Mott散射截面和Born近似下的广义振子强度计算模型得到.通过与压缩历史Monte Carlo方法的模拟计算结果及实验值的比较,结果表明,对于100 eV~10 keV范围的低能区电子,采用直接方法计算得到的电子背散射系数与实验值符合较好,直接方法比压缩历史方法更适合于能量在10 keV以下的电子输运计算.     

相对论重离子碰撞中有限碰撞时间对能量密度的影响

基于Monte Carlo模型"ODIN"分析了相对论重离子碰撞中能量刻度的时空发展.结果表明,由于有限的碰撞时间,实际达到的能量刻度明显低于Bjorken的估计值.     

利用宇宙线大气簇射的时空特征分辨原初γ射线

利用Monte Carlo模拟程序模拟甚高能区原初γ射线和质子大气簇射过程,分析γ射线和质子簇射在观测平面上的时空结构,利用大气簇射次级粒子到达时间涨落和空间横向分布特征,进行原初γ射线成分和质子成分的有效分辨,为中意合作羊八井ARGO实验进行γ点源的寻找提供了降低强子本底的方法,该工作还估计了该分辨方法对γ信号显著性的影响.     

利用MC模拟研究MRPC性能

利用Monte Carlo方法模拟了多气隙电阻板室(MRPC)的气体雪崩和信号收集过程,通过模拟与实验数据的比较分析,研究了MRPC粒子探测效率和信号幅度等性能,并讨论了一些外部条件的影响.     

Monte Carlo方法模拟光在生物组织中传播的新进展

Monte Carlo方法已被广泛用于模拟复杂的随机介质如生物组织中光的辐射传输.在生物光子应用中,早期的Monte Carlo模拟模型忽略了光在组织中传输的波动性,而用中性粒子光子包来模拟其传播过程.然而,许多光学诊断技术是基于光在组织中的偏振效应和多重散射的相干性来揭示组织的生理和病理信息,这就要涉及光辐射的波动性.本文阐述了用Monte Carlo方法模拟光在生物组织中传播的最新进展.     

Monte Carlo-based tail exponent estimator

In this paper we propose a new approach to estimation of the tail exponent in financial stock markets. We begin the study with the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations, we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce a Monte Carlo-based method of estimation for the tail exponent. Our proposed method is not sensitive to the choice of tail size and works well also on small data samples. The new estimator also gives unbiased results with symmetrical confidence intervals. Finally, we demonstrate the power of our estimator on the international world stock market indices. On the two separate periods of 2002-2005 and 2006-2009, we estimate the tail exponent.     

Radial distribution function in a diffusion Monte Carlo simulation of a Fermion fluid between the ideal gas and the Jellium model

We study, through the diffusion Monte Carlo method, a spin one-half fermion fluid, in thethree dimensional Euclidean space, at zero temperature. The point particles, immersed in auniform “neutralizing” background, interact with a pair-potential which can becontinuously changed from zero to the Coulomb potential depending on aparameter μ. We determine the radial distribution functions of thesystem for various values of density, μ, and polarization. We discussabout the importance, in a computer experiment, of the choice of suitable estimators tomeasure a physical quantity. The radial distribution function is determined through theusual histrogram estimator and through an estimator determined via the use of the Hellmannand Feynman theorem. In a diffusion Monte Carlo simulation the latter route introduces anew bias to the measure of the radial distribution function due to the choice of theauxiliary function. This bias is independent from the usual one due to the choice of thetrial wavefunction. A brief account of the results from this study were presented in arecent communication [R. Fantoni, Solid State Commun. 159, 106 (2013)].     

Joint estimation of the lognormal-Rician atmospheric turbulence model by the generalized method of moments

The generalized method of moments (GMM) is introduced in the framework of estimating the lognormal-Rician parameters. This GMM approach provides a systematic procedure for finding the moment-based parameter estimators. The GMM estimator can estimate the two shaping parameters of the lognormal-Rician PDF jointly. The asymptotic performance of the GMM estimator is compared with Monte Carlo simulation results. The results show that the GMM approach can lead to estimators with satisfactory performance over a wide range of channel conditions. The proposed method can be easily applied to both noiseless and noisy environments.     

低能电子在样品中散射的蒙特卡罗方法模拟

本文从已给物理假设及反应机制出发,叙述了电子在样品中散射的蒙特卡罗模拟方法,由于问题本身的特殊性,采用了指向概率和统计估计相结合的方法,得到了满意的结果。     

蒙特卡罗临界计算全局计数效率新算法研究

为提高蒙特卡罗临界计算时全局计数的整体效率, 对比分析了新提出的均匀计数密度算法、均匀径迹数密度算法和原有的均匀裂变点算法. 以大亚湾核反应堆pin-by-pin模型的全局体平均通量计数和中子沉积能计数为例, 前两种算法较均匀裂变点算法都获得了整体效率的提高. 上述算法已经在自主开发的并行蒙特卡罗输运程序JMCT上予以实现.     

On the sampling distribution of Allan factor estimator for a homogeneous Poisson process and its use to test inhomogeneities at multiple scales

The Allan factor (AF) is a statistic widely used to assess if the rate of occurrences of an event tends to cluster and show persistence in a range of space and/or time scales. For a homogeneous Poisson process, the relationship between AF and the space/time is expected to be constant, thus denoting the lack of clustering and persistence in the occurrence process. However, in time series analysis, conclusions about the persistence of the underlying process have been usually drawn by visual inspection of the diagrams of AF estimates versus scale, without applying any formal statistical test. This study investigates the sampling distribution function of the AF estimator when the underlying process is homogeneous Poissonian. Monte Carlo simulations show that the distribution of the AF estimator is described by a gamma distribution whose mean and variance can be deduced by the delta method. Therefore, the derived analytical distribution of the AF estimator can be used to build a formal statistical test to evaluate the significance of the AF fluctuation against the Poissonian hypothesis across a range of space/time scales. As an example, we apply the AF-based test to analyse the time series of the number of rainfall observations exceeding fixed high thresholds in order to study the properties of the rate of occurrence of the extreme values over a wide range of time scales.     

Random walks in the space of conformations of toy proteins

Monte Carlo dynamics of the lattice toy protein of 48 monomers is interpreted as a random walk in an abstract (discrete) space of conformations. To test the geometry of this space, we examine the return probability P(T), which is the probability to find the polymer in the native state after T Monte Carlo steps, provided that it starts from the native state at the initial moment. Comparing computational data with the theoretical expressions for P(T) for random walks in a variety of different spaces, we show that conformation spaces of polymer loops may have nontrivial dimensions and exhibit negative curvature characteristics of Lobachevskii (hyperbolic) geometry.     

超高速摄影仪转镜运转可靠性数值与实验分析

为探讨分析转镜运转可靠性的数值方法,运用Monte Carlo理论并结合ANSYS参量化设计语言,根据小样本原理和可靠性理论建立了转镜运转可靠性分析数学模型.对转镜运转可靠性进行了数值分析,并对数值分析结果进行了实验验证.数值分析结果表明:转镜的功能函数值远大于零,最大应力与转镜转速的线性相关系数达0.9,镜体密度的相关系数值为0.15,最大应力、应变、位移的偏斜度和峭度值均为正值,统计结果服从正态分布呈右偏态,在95%的置信度水平下,转镜可靠度为0.999.这说明在理想状态下转镜的运转可靠性满足要求,转镜的最大应力值取决于转镜转速,并且镜体的密度对转镜最大应力有较大影响.转镜运转可靠性试验中没有转镜出现破坏,表明转镜运转可靠性数值分析方法是正确的,为转镜的运转可靠性分析提供一种可行、高效的数值分析手段.     

Statistical fluctuations for the noise current from random telegraph signals in semiconductor devices: Monte Carlo computer simulations and best fits

Random Telegraph Signals (RTS) has become a major source of variability in the electrical behavior of modern transistors. The major contribution of this work is a new model based on a Monte Carlo algorithm for the mechanisms leading to RTS noise in semiconductor devices. To describe the statistical sample noise current produced by Monte Carlo simulations we experiment with many possible fits using different functions. In order to perform this fitting we follow two distinct approaches: (a) by calculating the sampling moments and by direct substitution in assumed distributions and (b) by performing a non-linear fit using the Levenberg Marquardt algorithm. Our results show a breaking of gaussianity for the nanometer dimensions of deeply scaled technologies, and we show that the ECS peak function is the most appropriate distribution to fit suitably the data for these dimensions. Another relevant contribution is the study of how ballistic effects can change the current distribution. The results indicate an enlargement of the average and variance of the total current when ballistic effects are considered, which are analytically expressed as a function of relevant physical parameters of the semiconductor device.     

A suggested statistical test for measuring bivariate nonlinear dependence

We devise a new asymptotic statistical test to assess independence in bivariate continuous distributions. Our approach is based on the Cramér–von Mises test, in which the empirical process is viewed as the Kullback–Leibler divergence, that is, as the distance between the data under the independence hypothesis and the data empirically observed. We derive the theoretical characteristic function of the limit distribution of the test statistic and find the critical values through computer simulation. A Monte Carlo experiment is considered as assessing the validation and power performance of the test by assuming a bivariate nonlinear dependence structure with fat tails. Two extra examples, respectively, consider stationary and conditionally nonstationary series. Results confirm that our suggested test is consistent and powerful in the presence of bivariate nonlinear dependence even if the environment is non-Gaussian. Our case is illustrated with high-frequency data from stocks listed on the NYSE that recently experienced so-called mini-flash crashes.