Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

Abstract

Thermoluminescence (TL) is defined as a luminescence phenomenon that can be detected when an insulator or semiconductor is thermally stimulated. Defective crystals store radiation until they are stimulated. Thermoluminescence is a method of monitoring the absorbed dose of dosimeters. The irradiation crystal is heated to 500°C to display the absorbed dose as a luminescent light. The TL dosimetric properties of calcite obtained from nature were investigated in this study. Machine learning was also examined using Gaussian process regression (GPR) for stimulated TL characteristics. According to the experimental output, the TL glow curve had two main peaks located at 90°C and 240°C with good dosimetric properties. In the four regression models of GPR, the data for the heating rate of 3°C s-1 have the lowest residual.