基于概率密度函数的加权变换在能谱测量中的应用
Modern Applie Physics(2022)
Abstract
能谱处理算法是提升探测系统能量分辨率的重要方法之一.其中,种子局部平均(seeded localized averaging,SLA)算法是一种比较新颖的处理算法,采用平均计算的方式对多个道址的信号进行处理输出,但在处理对称双峰及偏峰时会出现峰位飘移及生成不存在的虚峰等问题.针对该问题通过赋予不同的权重、引入均值不等式和优化迭代参数等改进方法,提出了 一种基于概率密度函数迭代的加权平均变换(weighted average transform,WAT)算法,利用概率密度函数模型描述探测器的随机输入信号,在对符合设定分布的随机输入信号累积处理过程中,利用加权平均的计算方式来处理信号.WAT算法保留了 SLA算法原有的性质,还提高了非对称峰输入的能量分辨率,进一步提高了原始输入分布的适应性,解决了 SLA算法处理时双峰输入后出现虚峰及重合峰等问题,偏峰处理将半高宽由741改善为435,峰位未飘移且未出现虚峰.利用WAT算法,对输入信号为高斯分布、对数高斯分布及多峰分布的情况进行数值模拟,验证了 WAT算法用于能谱求解的有效性.
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