Developing Support Vector Machine Prediction Capabilities of Uranium Enrichment Based on Gamma-Gamma Coincidence Signatures

The primary difficulty of obtaining useful information from gamma-gamma coincidence spectra is the process of determining which spectral features are useful for drawing a conclusion. This is especially true for dense and complicated coincidence spectra, such as those of fission products from irradiated uranium samples. A set of binary classification support vector machine algorithms was trained with supervised learning on gamma-gamma coincidence data from irradiated uranium samples of natural, low, and high-enriched uranium. The resulting model will be capable of classifying uranium samples into the correct enrichment regime (natural, low, or high) based on their gamma-gamma coincidence spectra. Current results with the binary classification support vector machines indicate a 92% classification accuracy. This eliminates the need for a spectral analyst to parse through the data and manually determine which spectral features are indicative of enrichment. The basis functions used by the model to make classification decisions can also inform the analyst of less obvious spectral features that are indicative of enrichment. Ongoing work to improve classification accuracy is discussed.