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

Jino Mathew, Rohit Kshirsagar, Dzariff Z Abidin, James Griffin, Stratis Kanarachos, Jithin James, Miltiadis Alamaniotis, Michael E Fitzpatrick

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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 classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class. [Abstract copyright: © 2023. The Author(s).]
Original languageEnglish
Article number9948
Number of pages15
JournalScientific Reports
Issue number1
Early online date19 Jun 2023
Publication statusE-pub ahead of print - 19 Jun 2023

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This work received funding from the Nuclear Security Science Network (NuSec), United Kingdom. M. E. Fitzpatrick is grateful for funding from the Lloyd’s Register Foundation, a charitable foundation helping to protect life and property by supporting engineering-related education, public engagement and the application of research. The work was partially supported by the Consortium on Nuclear Security Technologies (CONNECT) funded by US DOE.


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