Correlation between figure of merit function and deviation in the assessment of kinetic parameters in realistic TLD-100 behavior using fully-connected neural networks

In this study, a realistic model was employed to research the LiF:Mg:Ti behavior (commercially known as TLD-100), a widely employed as thermoluminescent (TL) material in dosimetry services. The research was focused on exploring the association between the Figure of Merit (FOM) function and deviations from the true kinetic parameter values, that were obtained through fitting experimental measures. FOM function is the most used magnitude to measure the quality of a fit achieved between a mathematical model and experimental data. Indeed, FOM function is particularly relevant when the TL glow curve deconvolution method is implemented to obtain the kinetic parameters: Activation Energy E (eV), Frequency Factor s (s−1), value of kinetic order b, Temperature at which the intensity maximum TMax(oC), Intensity Maximum IMax (a.u.) and Distribution Width σ (eV) of every peak. FOM function is subject to degeneracy under certain conditions, this means that different sets of kinetic parameters can lead to similar FOM values. Furthermore, as the obtained kinetic parameters are deemed acceptable when the FOM value is below 5%, then the physical degeneracy is greater than the mathematical ones. This contribution provides a rigorous analysis of how the FOM values can be impacted by deviations between experimental and estimated data of kinetic parameters from the analysis process. This studio was carried out using a mathematical method based on Artificial Intelligence with a high capability to manage a large volume of data. Another advantage of this method is that the analysis to be extended to a wide range of experimental data. •Study of TLD-100 behavior as a thermoluminescent material in dosimetry services.•Relationship between FOM function and deviations from kinetic parameters.•FOM can be degenerate: different kinetic parameters can lead to similar FOM values.•Using AI-based math methods for extensive analysis of large experimental data.