中子深度分析反演算法研究

Study on Unfolding Algorithms for Neutron Depth Profiling

  • 摘要: 针对中子深度分析技术,研究几类反演算法:概率迭代法、奇异值分解求解最小二乘法、线性正则化方法及约束线性正则化法.开展相关数值实验,分别在欠定方程、超定方程情况下,对反演算法的结果进行比较.一般情况下几种算法都能得到较为理想的结果,概率迭代法和约束线性正则化法由于有迭代过程,因此在离子源强度发生阶跃处得到的结果并不理想.在超定方程情况下,当选取的反演范围较为随意时,线性正则化方法可能使得解的稳定性较差,而约束线性正则化法能够很好地抑制求解过程中的不稳定性.对实际测量能谱进行反演时,由于能谱有随机误差,线性正则化方法不能很好地抑制误差的影响,反演结果振荡较强,其余方法的结果与参考值符合很好.

     

    Abstract: Unfolding algorithms for neutron depth profiling:probability iteration, SVD solving least square, linear regularization(LR) and constrained linear reqularization(CLR) are studied. All algorithms are applied for both under-determined and over-determined equations, and results are compared and discussed. Due to iterative processes, probability iteration and CLR could not work well in the case that sources intensities change sharply. LR is unstable as unfolding range is chosen arbitrarily, which can be constrained by CLR. A practical spectrum of NDP experiment is unfolded by algorithms. LR could not restrain stochastic errors caused by statistical fluctuation, while other algorithms show good unfolding results and agree well with references.

     

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