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Maximization Of Representativity Factors For Experimental Planning Of Cross-section Measurements: An Information Theoretic Approach

机译:横截面测量实验计划中代表性因子的最大化:一种信息理论方法

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The similarity between two nuclear systems is expressed by the representativity factors and maximizing its value to unity reduces the uncertainty from its existing value. As the representativity factor is a function of covariance matrix of neutron cross-sections, the inherent systematic uncertainty in neutron cross-sections inhibits it from approaching unity and hence statistical procedures have to be resorted to minimize the systematic uncertainty. As the conventional statistical techniques fail when systematic uncertainty is dominant, we propose an entropy based information theoretic approach of maximizing the mutual information by the knowledge of bounds for the correlated elements. We show that maximizing the mutual information and the representativity factors express the similar phenomena of uncertainty reduction. We estimated the bounds for the correlated elements of the correlation matrix for minor actinides and show how the systematic uncertainty is reduced when lower bound values are considered. These lower bound values aid in experimental planning for future measurement of cross-sections with reduced systematic uncertainty.
机译:两个核系统之间的相似性由代表性因子来表示,并且将其值最大化至统一可降低其现有值带来的不确定性。由于代表性因子是中子截面协方差矩阵的函数,因此中子截面固有的系统不确定性使其无法趋于统一,因此必须采用统计程序来最小化系统不确定性。由于传统的统计技术在系统不确定性占主导地位时会失败,因此,我们提出了一种基于熵的信息理论方法,该方法通过相关元素的边界知识最大化互信息。我们表明,最大化互信息性和代表性因素表达了不确定性降低的相似现象。我们估算了次act系元素的相关矩阵相关元素的范围,并显示了当考虑下限值时如何减少系统不确定性。这些下限值有助于实验计划,以便将来在测量横截面时减少系统不确定性。

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