Bayesian Updating of Material Balances Covariance Matrices Using Training Data

The main quantitative measure of nuclear safeguards effec-tiveness is nuclear material accounting (NMA), which con-sists of sequences of measured material balances that should be close to zero if there is no loss of special nuclear material such as Pu. NMA is essentially “accounting with measure-ment errors, ” which arise from good, but imperfect, measure-ments. The covariance matrix ΣMB of a sequence of material balances is the key quantity that determines the probability to detect loss. There is a recent push to include process moni-toring (PM) data along with material balances from NMA in new schemes to monitor for material loss. PM data includes near-real-time measurements by the operator to monitor and control process operations. One concern regarding PM data is the need to estimate normal behavior of PM residuals, which requires a training period prior to ongoing testing for mate-rial loss. Assuming that a training period is used for PM data prior to its use in statistical testing for loss, that same training period could also be used for improving the estimate of ΣMB that is used in NMA. We consider updating ΣMB using training data with a Bayesian approach. A simulation study assesses the improvement gained with larger amounts of training data. 1