Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks

The thermoluminescence (TL) method is one of the most widely used techniques in various studies, including dosimetric applications, dating of archaeological and geological materials, luminescence spectroscopy of certain insulating or semiconducting phosphors, and the detection of ionizing radiation damage. This study examines the TL properties of plagioclase, a feldspar group mineral, focusing on its dose–response behavior, kinetic parameters, and glow curve characteristics. TL measurements of plagioclase samples were carried out with different ionizing radiation doses ranging from 0.1 to 550 Gy. The results show a strong linear dose–response relationship in the 0.3–550 Gy range, with no evidence of saturation or supralinearity. A computerized glow curve deconvolution (CGCD) analysis revealed that the TL glow curve of the mineral consists of five distinct TL peaks with activation energies ranging from 0.842 eV to 0.890 eV and obeying general order kinetics. In addition, an artificial neural network (ANN) model was developed to predict TL glow curves using three optimization algorithms, including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Among these, the BR algorithm demonstrated the best performance with an accuracy value of 0.99915, a Mean Absolute Error (MAE) of 2.34 × 10⁻³, and a Mean Squared Error (MSE) of 3.82 × 10⁻⁵, outperforming LM and SCG in in terms of generalization and accuracy. The findings of this study demonstrate the effectiveness of combining TL analysis with ANN-based modelling for accurate dose–response predictions and the improved luminescence characterization of plagioclase, supporting the applications of luminescence studies in radiation dosimetry and geochronology.