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Scientists say they have developed a new artificial intelligence system capable of predicting uranium enrichment levels with extraordinary precision while identifying different types of nuclear material using gamma spectroscopy data. The findings, based on tests of three machine-learning models—XGBoost, CatBoost and LightGBM—could offer the U.N. nuclear watchdog valuable new tools to strengthen its safeguards under the Nuclear Non-Proliferation Treaty (NPT), according to researchers at the University of Sharjah.

The study, published in the Annals of Nuclear Energy, found that all three models performed perfectly in identifying uranium categories, recording 100% accuracy, precision, recall and F1-score.

The research introduces a dual-task machine learning framework that simultaneously predicts uranium-235 (235U) enrichment levels and classifies nuclear material types using gamma spectroscopy data.

The three gradient boosting models were trained on structured datasets derived from IAEA benchmark simulations of high-purity germanium (HPGe) detector outputs.

"The regression component achieved state-of-the-art performance, with XGBoost yielding a mean squared error (MSE) of 0.0019, a mean absolute error (MAE) of 0.0281 and a coefficient of determination (R²) of 1.0000," the authors write. "LightGBM and CatBoost followed with R² values of 0.99999 and 0.9971, respectively."

Among the three models, XGBoost delivered the strongest performance in predicting enrichment levels, achieving an almost negligible prediction error and a perfect R² score of 1.0000.

The authors report that even LightGBM attained nearly identical accuracy while offering faster computation times.

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