核数据评估管道的概念和软件实现
Conception and software implementation of a nuclear data evaluation pipeline
摘要：
我们讨论了核数据评估管道的设计和软件实现，该管道应用于使用核模型代码结合相关实验数据对$^{56}$ Fe在分辨共振区以上的中子诱导截面进行完全可再现的评估。重点是管道的数学和技术方面，而不是对$^{56}$ Fe的评估，这是试探性的。详细讨论了在流水线中组合和使用的数学构建块。实验数据，系统和统计误差，模型参数和缺陷的统一表示，使广义最小二乘法（GLS）及其自然扩展，Levenberg-Marquardt（LM）算法，在大量的实验数据的应用。为核数据评估量身定制的LM算法考虑了精确的非线性物理模型，以确定核数量的最佳估计。相关的不确定性信息来自于后验分布的最大值处的泰勒展开。我们还讨论了管道的IT（=信息技术）的积木，如那些有效地管理和检索实验数据的EXFOR库和分布计算的科学集群。依靠数学和IT构建模块，我们详细说明了管道中执行评估的步骤顺序，例如检索实验数据，使用边际似然优化（MLO）和筛选数千个TPERS参数（包括能量相关参数的高斯过程先验）后，校正实验不确定性，使用LM算法拟合约150个参数。管道的代码，包括手册和简化安装的Dockerfile，可在www.nucleardata.com上获得。
英文摘要：
We discuss the design and software implementation of a nuclear data evaluation pipeline applied for a fully reproducible evaluation of neutron-induced cross sections of $^{56}$Fe above the resolved resonance region using the nuclear model code TALYS combined with relevant experimental data. The emphasis is on the mathematical and technical aspects of the pipeline and not on the evaluation of $^{56}$Fe, which is tentative. The mathematical building blocks combined and employed in the pipeline are discussed in detail. A unified representation of experimental data, systematic and statistical errors, model parameters and defects enables the application of the Generalized Least Squares (GLS) and its natural extension, the Levenberg-Marquardt (LM) algorithm, on a large collection of experimental data. The LM algorithm tailored to nuclear data evaluation accounts for the exact non-linear physics model to determine best estimates of nuclear quantities. Associated uncertainty information is derived from a Taylor expansion at the maximum of the posterior distribution. We also discuss the pipeline in terms of its IT (=information technology) building blocks, such as those to efficiently manage and retrieve experimental data of the EXFOR library and to distribute computations on a scientific cluster. Relying on the mathematical and IT building blocks, we elaborate on the sequence of steps in the pipeline to perform the evaluation, such as the retrieval of experimental data, the correction of experimental uncertainties using marginal likelihood optimization (MLO) and after a screening of thousand TALYS parameters -- including Gaussian process priors on energy dependent parameters -- the fitting of about 150 parameters using the LM algorithm. The code of the pipeline including a manual and a Dockerfile for a simplified installation is available at www.nucleardata.com.
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