Evaluated nuclear data uncertainties are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty (USU) that limit the accuracy to which these physical quantities can be determined. The objective of our work has been to develop procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. We conclude that the presence of USU may be revealed, and estimates of magnitudes made, through quantitative analyses. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU. It then describes numerical procedures to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Realistic examples are described to illustrate these procedures and demonstrate their outcomes as well as limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community.

中文翻译：

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实验核数据中未识别的不确定性来源 (USU)

经评估的核数据不确定性通常被认为是不切实际的，最常见的原因是它们被认为太小。近年来，这个问题在应用核科学中的影响得到了广泛讨论。通常建议的原因是：对特定误差成分的估计不佳、忽视不确定性相关性和忽视已知误差源。然而，据报道，已经对所有已知误差源进行了非常仔细、客观的评估，并提供了现实的误差幅度和相关性，但由此产生的评估不确定性似乎仍与观察到的预测平均值的分散不一致。这些差异可能归因于重大的未识别的不确定性来源 (USU)，这些来源限制了确定这些物理量的准确性。我们工作的目标是制定程序，在涉及实验输入数据的核数据评估中揭示和包括 USU 估计。我们得出的结论是，可以通过定量分析揭示 USU 的存在，并进行震级估计。本文确定了评估人员在确定 USU 存在时可以探索的几个具体线索。然后描述了生成 USU 量级定量估计的数值程序。使这些程序可行的关键要求是，出于统计原因，有足够数量的数据点可用，并且实验者应提供有关测量的其他支持信息。描述了真实的例子来说明这些程序并展示它们的结果以及局限性。