2024/10/02 by Paul Lartaud, Lartaud, Paul, Philippe Humbert +3
Engineering · Physics and Astronomy · #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Nuclear Physics and Applications #Nuclear reactor physics and engineering #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.2410.01522
openalex publication_date 2024/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.