基于概率迭代的NDP反演方法

An Unfolding Method of NDP Based on Probability Iteration

  • 摘要: 针对中子深度分析(NDP)技术,基于概率迭代思想,推导一种迭代反演方法,对其和线性正则化方法在NDP反演问题中的应用进行比较.未引入计数随机误差时,两者都能得到较好的反演结果,迭代法的结果是非负的,线性正则化方法在源强度发生阶跃处得到的结果更好.在考虑随机误差存在的情况下,迭代法的效果更佳.同时,研究了迭代矩阵对结果的影响,矩阵元的指数放大可以实现迭代过程的加速.

     

    Abstract: For NDP,we produce an iteration method derived from probability iteration.It is compared with linear regularization(LR) method for unfolding of NDP.Both produce good results without counts random errors.Iteration method's result is none-negative.LR method is better at the position where source distributions change steeply.Iteration method produce better results as we consider count random errors.It shows that amplifying matrix elements exponentially brings acceleration.

     

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