Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis

TL;DR

This paper introduces a multi-output Gaussian process framework to improve neutron correlation predictions and perform inverse uncertainty quantification, addressing the challenges of noise and bias in neutron noise analysis for fissile materials.

Contribution

It develops a novel multi-output Gaussian process model for surrogate modeling and inverse UQ in neutron noise analysis, accounting for measurement noise and model bias.

Findings

Enhanced neutron correlation predictions with quantified errors

Effective inverse uncertainty quantification incorporating measurement noise

Robust surrogate models for fissile material characterization

Abstract

In a fissile material, the inherent multiplicity of neutrons born through induced fissions leads to correlations in their detection statistics. The correlations between neutrons can be used to trace back some characteristics of the fissile material. This technique known as neutron noise analysis has applications in nuclear safeguards or waste identification. It provides a non-destructive examination method for an unknown fissile material. This is an example of an inverse problem where the cause is inferred from observations of the consequences. However, neutron correlation measurements are often noisy because of the stochastic nature of the underlying processes. This makes the resolution of the inverse problem more complex since the measurements are strongly dependent on the material characteristics. A minor change in the material properties can lead to very different outputs. Such an inverse problem is said to be ill-posed. For an ill-posed inverse problem the inverse uncertainty quantification is crucial. Indeed, seemingly low noise in the data can lead to strong uncertainties in the estimation of the material properties. Moreover, the analytical framework commonly used to describe neutron correlations relies on strong physical assumptions and is thus inherently biased. This paper addresses dual goals. Firstly, surrogate models are used to improve neutron correlations predictions and quantify the errors on those predictions. Then, the inverse uncertainty quantification is performed to include the impact of measurement error alongside the residual model bias.