A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data.

ABSTRACT: The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for cl

SUBMITTER: Mathew J 

PROVIDER: S-EPMC10279725 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature