Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics

Abstract

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T2), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

Analysis of solid uranium particulates on cotton swipes with an automated microextraction-ICP-MS system

“…1,[15][16][17][18][19] Automated secondary electron microscopy systems, like TESCAN integrated mineral analyzer (TIMA), have been used to map samples and can obtain chemical and textural information for a 1 cm 2 section of a geological sample in 30 minutes. [20][21][22] These improvements in automation have proven to be benecial to nuclear safeguards programs by increasing sample throughput and decreasing personnel required for sample analyses while maintaining low measurement uncertainties.…”

Special Issue “2021 Selected Papers from Algorithms’ Editorial Board Members”

“…Finally, the last accepted review paper [12] deals with pre-detonation nuclear forensics and focuses on analyses of particle size, shape and texture signatures that could give an information on the provenance of interdicted materials. In this paper, the authors describe statistical issues and approaches in using quantitative morphological measurements such as the particle size or shape to infer production conditions.…”