Kari Sentz

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. This review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

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.

Morphological features have proven to be a useful signature in determining the process histories of uranium oxides. Historically, morphological analysis has relied on using image analysis software to segment fully visible particles in Scanning Electron Microscopy (SEM) images of a particular sample and then compute attributes such as circularity, area, perimeter, ellipse aspect ratio of these segmented particles. One such software is Morphological Analysis for MAterial (MAMA) developed by Los Alamos National Laboratory. MAMA provides both segmentation and quantification functionality. Unfortunately, SEM images of nuclear materials can be difficult to segment due to overlapping particles, charging effects, and image clarity. It requires significant user inputs, which is time-consuming and tedious, to segment only fully visible particles. In this study, an alternative segmentation method, using a deep learning model, was used to segment fully visible particles. The deep learning model used in this study is a modified version of a well-known segmentation model in the computer vision community referred to as U-net. This model was able to produce the segmentation results similar to manual segmentation results obtained using MAMA with at least 85% accuracy in intersection over union metric. Furthermore, the model achieved a similar statistical relevance as manual segmentation under Kolmogorov-Smirnov (K-S) test. [Display omitted] •Segmentation of fully visible particles using a deep learning model.•Similar segmentation results between the proposed model and manual segmentation.•Differentiating materials processing histories using morphological features of the automatically segmented particles.

An important part of processing elicited numerical inputs is an ability to quantitatively decode natural-language words that are commonly used to express or modify numerical values. These include 'about', 'around', 'almost', 'exactly', 'nearly', 'below', 'at least', 'order of, etc., which are collectively known as approximators or numerical hedges. Figuring out the quantitative implications of these expressions for the uncertainty of numerical quantities is important for being able to understand, for example, what is actually being reported by a patient who says a headache has lasted for "about 7 days", and how we should translate the patient's report into uncertainty about the duration. We used Amazon Mechanical Turk to empirically identify the implications of various approximators common in English. To evaluate the numerical range implied by each approximator, we analyzed paired statements differing only in the approximator used in numerical expressions. Despite often considerable diversity, there were several statistically significant findings, but far less quantitative variation implied by the approximators than might have been expected. The numerical implication of different approximators interacts with the magnitude and roundness of the nominal quantity. This investigation strategy generalizes easily to languages other than English. (C) 2014 Elsevier Inc. All rights reserved.

The current challenge of nuclear weapon stockpile certification is to assess the reliability of complex, high-consequent, and aging systems without the benefit of full-system test data. In the absence of full-system testing, disparate kinds of information are used to inform certification assessments such as archival data, experimental data on partial systems, data on related or similar systems, computer models and simulations, and expert knowledge. In some instances, data can be scarce and information incomplete. The challenge of Quantification of Margins and Uncertainties (QMU) is to develop a methodology to support decision-making in this informational context. Given the difficulty presented by mixed and incomplete information, we contend that the uncertainty representation for the QMU methodology should be expanded to include more general characterizations that reflect imperfect information. One type of generalized uncertainty representation, known as probability bounds analysis, constitutes the union of probability theory and interval analysis where a class of distributions is defined by two bounding distributions. This has the advantage of rigorously bounding the uncertainty when inputs are imperfectly known. We argue for the inclusion of probability bounds analysis as one of many tools that are relevant for QMU and demonstrate its usefulness as compared to other methods in a reliability example with imperfect input information. (C) 2011 Elsevier Ltd. All rights reserved.