Machine learning-based interpolation of density-dependent full-energy peak efficiency in Marinelli geometry using...

This study investigates the prediction of density-dependent full-energy peak efficiency (FEPE) behavior in Marinelli geometry using ensemble-based machine-learning techniques trained on experimentally derived NaI(Tl) detector calibration data. Experimental FEPE curves corresponding to 0.7, 1.0, and 1.3 g cm^-3 sample densities were obtained using mixed gamma-ray standards in the 60-1837 keV energy range. Experimentally derived FEPE datasets were represented using logarithmic polynomial efficiency functions in order to preserve the energy-dependent detector-response structure prior to machine-learning-based interpolation analysis. Gradient Boosting regression and Random Forest regression models were trained using the 0.7 and 1.3 g cm^-3 datasets, while the independently measured 1.0 g cm^-3 dataset was withheld from training and used exclusively for prediction and validation. Model performance was evaluated using MAE, RMSE, MAPE, and the coefficient of determination (R²). The results demonstrated that both ensemble-learning approaches successfully reproduced the density-dependent detector response with high consistency. The results demonstrated that both ensemble-learning approaches successfully reproduced the density-dependent detector response with high consistency, with Gradient Boosting yielding lower prediction errors than Random Forest across the investigated calibration energies. Although conventional linear and log-linear interpolation yielded lower numerical errors for the present smooth, symmetrically bounded density configuration, both ensemble-learning models successfully reconstructed the independently measured intermediate-density FEPE response. The study demonstrates that ensemble-learning approaches can provide a reliable data-driven framework for reconstructing unseen intermediate-density FEPE responses from experimentally measured boundary-density calibration data within an experimentally characterized density range.