Abstract
We present a first study of using artificial neural networks to estimate the fading time and irradiation dose using glow curve data from LiF thermoluminescent (TL) dosemeters. The resulting uncertainties in the inference process are compared to those obtained in previous studies without machine learning algorithms. The current study is based on measurement and simulated data using an effective model of the kinetic parameters. We show that the resulting uncertainties of the estimated quantities can be significantly reduced with the machine learning algorithm applied: fading times of up to 30 days can be predicted with an uncertainty of up to 10%, irradiation doses larger than 1 mSv can be estimated with an uncertainty of up to 10% for batch-calibrated dosemeters.
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