# Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics
###### Abstract
One open question is whether machine learning (ML) methods can clearly recognize ADU, AUC, MDU, SDU when all 3 pathways are represented in the data and also when only one pathway (baseline condition only, or either of the other two conditions) is represented in the data. Another inference goal is then to determine factors that impact particle morphology, such as calcination temperature, production pathway, and impurities for a given precipitation condition. Figure 1: SEM image from the calcination of MDU at 400 \({}^{\circ}\)C. The left image contains the raw unprocessed image of the particle surface morphology at 50,00\(\times\). The image on the right was manually segmented using MAMA software. The blue regions were the individual particle features that had clearly defined boundaries and were used to quantitatively characterize the morphology of the material. This paper focuses on ML options applied to the 22 MAMA measurements per particle to recognize ADU, AUC, MDU, and SDU precipitation conditions when 1, 2, or 3 pathways are represented in the data. Data from [2] are analyzed to estimate the correct classification rate (CCR) in recognizing ADU, AUC, SDU, and MDU. For completeness, it is noted here that data from [1] are vector representations of images (using unsupervised vector quantized variational autoencoding) either of length 256 or 1000 that are derived from each SEM image [1]. These vector representations of images have been shown to have a CCR of approximately 0.80 depending on the particular data subset used [1]. Because the CCR depends on the particular data subset analyzed, this paper does not directly compare the CCR when using the 256-length or 1000-length vectors from [1] for each image to the CCR when using 22-length MAMA measurements from each segmented particle from its respective SEM image. Instead, this paper reviews statistical issues in using the 22-length MAMA measurements per particle, and applies ML options to the 22-length MAMA measurements per segmented particle, with the ADU, AUC, MDU, and SDU precipitation conditions and the processing pathways involving UO3, U3O8, and UO2, at 400 \({}^{\circ}\)C, 800 \({}^{\circ}\)Cand 510 \({}^{\circ}\)C (in H2) all known (supervised learning). Data from [2] are 22 morphological measurements produced from MAMA software (morphological analysis for material attribution) such as particle area, aspect ratio, and circularity (Appendix A). Data from [2] can be analyzed per particle or per image. Available data include 10s to 100s of MAMA measurements of particles per image, with a total of  
Figure 2: Example SEM images, such as those used in [1,2], at the listed magnifications (right), for MDU, ADU, SDU, and AUC throughout the 400 \({}^{\circ}\)C (UO3), 800 \({}^{\circ}\)C (U3O8), and 510 \({}^{\circ}\)C (UO2) production pathways. 9255 particle measurements (2258, 2436, 2263, and 2298 sets of 23 MAMA measurements of particles for ADU, AUC, SDU, and MDU, respectively). For ADU, there are 16, 12, and 14 images with an average of 47.3, 62.5, and 53.6 particles per image, for ADU1, ADU2, ADU3 (ADU1, ADU2, ADU3 denote pathways U3O8, UO2, or UO3, respectively). For AUC, there are 15, 8, and 10 images with an average of 57.7, 99.4, and 77.5 particles per image, for AUC1, AUC2, AUC3, respectively. For SDU, there are 8, 8, and 7 images with an average of 94, 94.5, and 107.9 particles per image, for SDU1, SDU2, SDU3, respectively. For MDU, there are 16, 19, and 18 images with an average of 46.9, 40.2, and 43.6 particles per image for MDU1, MDU2, MDU3, respectively. The results presented here are by particle because of the limited number of images for each precipitation condition and pathway [1; 2; 3; 4; 5]. This paper is organized as follows. Section 2 presents CCR results for nine ML methods.