QUANTIFYING THE EFFECT OF MEASUREMENT UNCERTAINTY ON A
SEPERATED PLUTONIUM ATTRIBUTION METHODOLOGY

Ongoing work at Texas A&M University has produced a nuclear forensics methodology that can attribute a separated plutonium sample’s reactor-type, fuel burnup, and time since irradiation (TSI). The attribution of these three parameters is performed using two models trained with machine learning, a classification model for the reactor-type and a regression model for fuel burnup. The TSI is calculated analytically using the predicted reactor-type and fuel burnup. Sets of intra-element isotope ratios are the features used in both models. The training data used in producing the models is sampled from a library of Monte Carlo N-Particle (MCNP) fuel burnup simulations that have been performed for a set of reactors of interest. For the validation of the model performance multiple irradiation campaigns were conducted to produce physical samples that could be attributed. These campaigns involved irradiating uranium samples of varying initial enrichment levels at the High Flux Isotope Reactor (HFIR) and the University of Missouri Research Reactor (MURR). These include a depleted UO2 sample irradiated at HFIR in a pseudofast neutron spectrum, and a natural UO2 and low enriched UO2 (3.44 wt-%) sample irradiated at MURR in a thermal neutron spectrum. Subsequently the plutonium produced was separated and the isotopic concentrations determined. The use of simulated data for the model production, and then physical data in the use of the model introduces an unavoidable amount of incongruency, as there will always be differences between these two. Additionally, both sets of data introduce their own sources of uncertainty, and characterizing this uncertainty is important for judging the ultimate capabilities of this attribution methodology. A study was performed to use the validation data’s measurement uncertainties to study the effect that variance in the input can have on model predictions. To do this, a set of test data was produced for each validation plutonium sample by sampling each isotope ratio from a normal distribution with the measured mean and variance for that isotope ratio, and then predictions were made with this data set to find how the predictions change with the natural variation in the measured isotope ratios values. By analyzing the extended uncertainty bounds the effect that measurement uncertainty has on the methodology’s prediction capability can be visualized.