Application Of Approximate Bayesian Computation In Neutron Multiplicity Measurements: Providing Uncertainty Distributions And Correlations In All The Assay-item Parameters

Tom L. Burr - Los Alamos National Laboratory
Andrea Favalli - Los Alamos National Laboratory
Andrea Favalli - Los Alamos National Laboratory
Brian Weaver - Los Alamos National Laboratory
Daniela Henzlova - Los Alamos National Laboratory
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Uncertainty quantification (UQ) for safeguards applications can be approached from physical first principles (“bottom-up”) or approached empirically by comparing measurements from different methods and/or laboratories (“top-down”). The two approaches can lead to different estimates of uncertainty, often with the bottom-up uncertainty estimate being smaller than the top-down uncertainty estimate; such a gap between the estimates is “dark uncertainty.” Currently, one component of dark uncertainty in neutron multiplicity measurements arises because uncertainty in nuclear data is ignored. One option to account for uncertainty in nuclear data is approximate Bayesian computation (ABC). This paper reviews ABC and illustrates how ABC can be applied in passive neutron multiplicity counting (PNMC) both with and without including the effects of errors in nuclear data. As a diagnostic, when an ABC-based interval for the true measurement error relative standard deviation (RSD) is constructed to contain approximately 95% of the true values, one can check whether the actual coverage is close to 95%. The performance of ABC illustrates potential advantages compared to current bottom-up and top-down approaches. The work presented in this paper was funded by the National Nuclear Security Administration of the Department of Energy, Office of International Nuclear Safeguards.​