Abstract
In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.
Highlights
In order to deter the proliferation of nuclear weapons, safeguards provide various technical measures that are used for the verification and the declarations made by the signatories to the Treaty on the Non-Proliferation of Nuclear Weapons, regarding their nuclear material and activities [1]
An important task within these safeguards is monitoring of spent fuel assemblies (SFAs) from nuclear power plants (NPPs), for detecting any eventual diversion of spent nuclear fuel for non-declared purposes
IAEA approved the use of the passive gamma emission tomography (PGET) instrument [2,3,4,5,6,7] in inspections
Summary
In order to deter the proliferation of nuclear weapons, safeguards provide various technical measures that are used for the verification and the declarations made by the signatories to the Treaty on the Non-Proliferation of Nuclear Weapons, regarding their nuclear material and activities [1]. Most of state-of-the-art algorithms considered to solve linear inverse image restoration problems (irrespective of the noise model) are either optimization or simulation-based methods. To the best of our knowledge, no work attempted to sample from such a highly-multimodel joint posterior distribution using an SPA sampler This allows for estimating the activity of spent-fuel, including the assessment of fuel rod presence/absence. This inverse problem is severely illconditioned because of A and prior regularization is necessary to promote the solution to be in a set of feasible intensities x To solve this problem, we propose a hierarchical Bayesian model and a sampling method to estimate the unknown model parameter
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