Exploring Laser Induced Breakdown Spectroscopy (LIBS) for Post-Detonation Nuclear Forensics Debris Analysis

In the unlikely but catastrophic event of a nuclear terrorist attack our government leadership will need reliable information to rapidly inform critical decisions. This research explores the use of Laser Induced Breakdown Spectroscopy (LIBS) as a potential analysis tool in the National Technical Nuclear Forensics process. The current state of post detonation nuclear forensics requires ground and air samples be collected and shipped to state-of-the-art laboratories for radiochemical analysis. The samples undergo many measurements and useable data is produced as these measurements are completed. This data flows back into the process to guide additional measurements and inform the process of narrowing down the origin of the nuclear materials. This is a time-consuming process in need of new analytical methods that can be performed in situ. It is clear that LIBS will not be able to perform all of the measurements needed but the intent of this project is to explore where a LIBS system deployed with a ground collection team could provide meaningful data more quickly than the traditional radiochemistry processes. My research will include calibrating and optimizing LIBS system at the United States Military Academy and conducting analysis of Trinitite (glass like debris from the first nuclear weapon test) and a surrogate material produced by University of Tennessee at Knoxville. The intent of the surrogate material is to be used during post-detonation nuclear forensics exercises. The analysis will include optimizing collection parameters for the glass-like samples, comparison of key constituents in Trinitite and the surrogate material, and characterizing the effects of sample non-homogeneity

Unsupervised Machine Learning Approaches to Nuclear Particle Type Classification

Historically, nuclear science and radiation detection fields of research used Pulse Shape Discrimination (PSD) to label gamma-ray and neutron interactions. However, PSD’s effectiveness relies greatly on the existence of distinguishable differences in an interaction’s measured pulse shape. In the fields of machine learning and data analytics, clustering algorithms provide ways to group samples with similar features without the need for labels. Clustering gamma-ray and neutron interactions may mitigate PSD’s pitfalls, since clustering methods view the total waveform rather than just the area under the tail and the total area under the pulse. However, traditional clustering methods, such as the k-means clustering algorithm, suffer from poor performance on high dimensional data. This study explores unsupervised machine learning methods using Deep Neural Networks (DNN) to cluster gamma-ray and neutron interaction measurements collected with an organic scintillation detector, in order to perform binary labeling of gamma-rays and neutrons. Using various network architectures, this research demonstrates the effectiveness of using autoencoder-based neural networks to cluster gamma-ray and neutron interactions when compared to shallow clustering algorithms. The results reveal the effectiveness of autoencoders on high energy gamma-ray and neutron pulses with an energy deposit greater than 0.80 MeVee whilst greatly outperforming k-means comparatively in all cases