Neutron noise analysis is a predominant technique for fissile matter identification with passive methods. Quantifying the uncertainties associated with the estimated nuclear parameters is crucial for decision-making. A conservative uncertainty quantification procedure is possible by solving a Bayesian inverse problem with the help of statistical surrogate models but generally leads to large uncertainties due to the surrogate models' errors. In this work, we develop two methods for robust uncertainty quantification in neutron and gamma noise analysis based on the resolution of Bayesian inverse problems. We show that the uncertainties can be reduced by including information on gamma correlations. The investigation of a joint analysis of the neutron and gamma observations is also conducted with the help of active learning strategies to fine-tune surrogate models. We test our methods on a model of the SILENE reactor core, using simulated and real-world measurements.

翻译：中子噪声分析是无源方法识别裂变材料的主要技术。量化与估计核参数相关的不确定性对决策制定至关重要。借助统计代理模型求解贝叶斯反问题可实现保守的不确定性量化，但由于代理模型误差通常会导致较大的不确定性。本研究基于贝叶斯反问题求解，开发了两种用于中子与伽马噪声分析的鲁棒不确定性量化方法。研究表明，通过纳入伽马关联信息可降低不确定性。同时借助主动学习策略优化代理模型，对中子与伽马观测的联合分析进行了研究。我们在SILENE反应堆堆芯模型上使用模拟和实测数据验证了所提方法。