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Influence of resonance parameters' correlations on the resonance integral uncertainty; 55Mn case

机译:共振参数的相关性对共振积分不确定性的影响; 5500万箱

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摘要

For nuclides with a large number of resonances the covariance matrix of resonance parameters can become very large and expensive to process in terms of the computation time. By converting covariance matrix of resonance parameters into covariance matrices of background cross-section in a more or less coarse group structure a considerable amount of computer time and memory can be saved. The question is how important is the information that is discarded in the process. First,the uncertainty of the 55Mn resonance integral was estimated in narrow resonance approximation for different levels of self-shielding using Bondarenko method by random sampling of resonance parameters according to their covariance matrices from two different 55Mn evaluations: one from Nuclear Research and Consultancy Group NRG (with large uncertainties but no correlations between resonances),the other from Oak Ridge National Laboratory (with smaller uncertainties but full covariance matrix). We have found out that if all (or at least significant part of the) resonance parameters are correlated,the resonance integral uncertainty greatly depends on the level of self-shielding. Second,it was shown that the commonly used 640-group SAND-II representation cannot describe the increase of the resonance integral uncertainty. A much finer energy mesh for the background covariance matrix would have to be used to take the resonance structure into account explicitly,but then the objective of a more compact data representation is lost.
机译:对于具有大量共振的核素,共振参数的协方差矩阵可能变得非常大,并且在计算时间方面处理起来很昂贵。通过将共振参数的协方差矩阵转换为或多或少的粗糙组结构中背景横截面的协方差矩阵,可以节省大量的计算机时间和内存。问题是在流程中丢弃的信息有多重要。首先,使用邦达连科(Bondarenko)方法,根据共振参数的随机方差矩阵,根据来自两个不同55Mn评估的协方差矩阵,对共振参数进行随机采样,以窄共振近似法估算55Mn共振积分的不确定性。 (不确定性较大,但共振之间没有相关性),另一种来自Oak Ridge国家实验室(不确定性较小,但协方差矩阵完整)。我们发现,如果所有(或至少大部分)谐振参数都相关,则谐振积分不确定性很大程度上取决于自屏蔽的水平。其次,表明常用的640组SAND-II表示不能描述共振积分不确定性的增加。必须使用用于背景协方差矩阵的更精细的能量网格来明确考虑共振结构,但是这样就失去了以更紧凑的数据表示的目的。

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