Key points are not available for this paper at this time.
Abstract This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. Predictions from various ML models were compared with results from the Phy-X/PSD program across different photon energies. The Gradient Boosting Regressor (GBR) model was identified as the most accurate model, achieving a testing set accuracy of 96.40%. This research showcases the potential of ML for efficiently and accurately estimating LACs, with the GBR model showing promise for applications in radiation detection and material science.
Nahool et al. (Sat,) studied this question.