## 1 Introduction  
Thermoluminescence (TL) is a widely utilized phenomenon in materials science and dosimetry, offering valuable insights into the luminescent properties of materials subjected to ionizing radiation. The TL process involves the emission of light as trapped electrons, previously excited by radiation, which are released upon heating and recombine at luminescent centers. This property makes TL a fundamental tool for dosimetry, archaeological dating, and environmental monitoring [1]. A critical parameter in TL research is the dose-response relationship, which defines how the TL signal intensity varies with the absorbed radiation dose. The linear dose-response range is especially important, as it ensures predictable and accurate measurements for practical applications [2, 3]. However, beyond a certain threshold, materials may exhibit nonlinear behaviors such as supralinearity or saturation, influenced by trap-filling limitations and recombination mechanisms [3; 4]. In recent years, the computerized glow curve deconvolution (CGCD) method has emerged as a pivotal analytical tool in TL research. CGCD enables the separation of overlapping glow peaks, which represent different trapping and recombination mechanisms, into their individual components. This method facilitates the extraction of kinetic parameters such as activation energy (E), the frequency factor (s), and kinetic order (b), which are critical for understanding the luminescence mechanisms within materials [5; 6]. For example, CGCD has been extensively applied to feldspar-group minerals, including plagioclase, which are widely studied due to their natural abundance and unique luminescence properties [7]. Artificial neural networks (ANNs) have recently gained attention as a powerful computational approach for TL glow curve analysis and prediction [8; 9; 10; 11]. ANN-based models can accurately simulate TL glow curves by learning the complex relationships between input parameters such as radiation dose, temperature, and kinetic parameters. Methods like Levenberg-Marquardt optimization, Bayesian Regularization, and Scaled Conjugate Gradient algorithms have been employed to optimize ANN performance, improving prediction accuracy and computational efficiency [10; 12; 13]. For instance, ANN-based systems have demonstrated remarkable success in estimating fading times and reconstructing dose-response relationships for luminescent materials like feldspars, outperforming traditional curve-fitting methods in terms of accuracy and robustness [14; 15]. The chemical composition of feldspars includes alkaline metals, aluminum, silica, and oxygen, and their general chemical formula has a complex structure. This complex structure is due to the presence of both monovalent and divalent cations that require heterovalent substitution. Therefore, feldspars are divided into three groups: plagioclase (albite: NaAlSi\({}_{3}\)O\({}_{8}\); anorthite: CaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)), alkaline (albite and all potassium feldspars: K/NaAlSi\({}_{3}\)O\({}_{8}\)), and barium feldspars (celisian: BaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)). Feldspars are found in almost all igneous and metamorphic rocks, although igneous rocks containing more silica usually contain K-feldspars and albite. Pegmatities are important sources of feldspar, and, due to their chemical evolution, often contain both K-feldspars and albite in the form of beautiful crystals that can grow up to several meters in length [16; 17]. Plagioclase, a key member of the feldspar group, is of particular interest in TL studies due to its complex crystal structure and potential for luminescent applications. Studies have shown that plagioclase minerals irradiated under controlled conditions exhibit distinct glow peaks that can be effectively analyzed using CGCD and ANN methods [7; 14]. By integrating these advanced analytical tools, researchers can gain a deeper understanding of the luminescent behavior of plagioclase and other feldspar minerals, paving the way for innovative applications in dosimetry and geochronology.