Uncertainty quantification in neutron and gamma time correlation measurements

TL;DR

This paper develops robust Bayesian methods for quantifying uncertainties in neutron and gamma time correlation measurements, improving fissile material identification accuracy by incorporating gamma correlation data and active learning for surrogate model refinement.

Contribution

It introduces two new methods for uncertainty quantification in neutron-gamma analysis using Bayesian inverse problems and active learning, enhancing the reliability of nuclear parameter estimation.

Findings

Including gamma correlation information reduces uncertainties.

Active learning improves surrogate model accuracy.

Methods tested successfully on SILENE reactor data.

Abstract

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.