Abstract
In the nondestructive assay of special nuclear materials, isotopic determination based on the analysis of high-purity Ge high-resolution gamma-ray spectra plays a pivotal role. The isotopic results, which are based on a relative measurement of peak intensities, are used to interpret data acquired using quantitative (mass) nondestructive assay methods. In the case of plutonium measurements, this includes both passive and active neutron techniques, and also calorimetry (heat). Historically, precision estimates on the Pu- isotopic composition have been generated, on a case-by-case basis, by least squares fitting algorithms embedded in the analysis codes, but all other sources of uncertainty have generally been estimated using a top-down approach by item category. A largely neglected consideration is the covariance between the reported abundances and derived quantities – notably Pu-239-effective mass, Pu-240-effective mass and specific thermal power. In this work we study the covariance structure by statistically evaluating repeat spectrum measurements of two known low burn-up heavily filtered Pu items reduced using the spectrum analysis code FRAM 5.2 and alternate standard parameter sets. For routine measurements, however, only a single spectrum from an item will typically be acquired. We therefore discuss how to generate the sought after covariance information from an individual spectrum by spectrum re-sampling, an approach which could be added to provide item specific reports, and compare the two approaches to test its reliability.
