A probabilistic inverse prediction method for predicting plutonium processing conditions

Abstract

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

“…Ries et al (2019) converted the many particle measurements from SEM images to cumulative distribution functions and used these to discriminate among processing conditions, effectively exploiting the information in distributional measurements to improve results. Ries et al (2023) formalized this process into a functional inverse prediction (FIP) framework that was then applied to a full bench-scale PuO 2 experiment, while Ausdemore et al (2022) inverse-predicted PuO 2 processing conditions using Bayesian MARS techniques (Francom et al, 2018).…”