核数据评价的迭代贝叶斯蒙特卡罗方法
Iterative Bayesian Monte Carlo for nuclear data evaluation
摘要：
在这项工作中，我们将探讨使用迭代贝叶斯蒙特卡罗（IBM）的过程中的Talys评估核数据库（TENDL）框架内的核数据评估。为了识别再现所选实验数据的模型和参数组合，对TAPIS代码中实现的不同物理模型进行采样并同时改变，以产生具有独特模型组合的随机输入文件。所有考虑的模型被假定为是平等的先验。这些模型的参数，然后同时使用的TPERS代码系统，以产生一组随机ENDF文件，其被处理成x-y表与选择的实验数据进行比较，从EXFOR数据库中的贝叶斯框架。为了改善我们对实验数据的拟合，我们迭代地更新我们的“最佳”文件-最大化似然函数的文件-通过围绕该文件重新采样模型参数。所提出的方法已被应用于评价p+Cd-111和Co-59之间的1 - 100 MeV的入射能区。最后，将调整后的文件与EXFOR数据库中的实验数据以及TENDL-2017和JENDL-4.0/HE核数据库中的评价进行比较。
英文摘要：
In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM) procedure for nuclear data evaluation within a Talys Evaluated Nuclear data Library (TENDL) framework. In order to identify the model and parameter combinations that reproduce selected experimental data, different physical models implemented within the TALYS code, were sampled and varied simultaneously to produce random input files with unique model combinations. All the models considered were assumed to be equal a priori. Parameters to these models were then varied simultaneously using the TALYS code system to produce a set of random ENDF files which were processed into x-y tables for comparison with selected experimental data from the EXFOR database within a Bayesian framework. To improve our fit to experimental data, we iteratively update our 'best' file - the file that maximises the likelihood function - by re-sampling model parameters around this file. The method proposed has been applied for the evaluation of p+Cd-111 and Co-59 between 1 - 100 MeV incident energy region. Finally, the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2017 and JENDL-4.0/HE nuclear data libraries.
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