No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry
This work addresses exposure date estimation for personal dosimetry in radiation monitoring, representing an incremental improvement over existing techniques.
The paper tackled the problem of estimating the date of a single irradiation in thermoluminescence dosimetry using deep learning, achieving an uncertainty of 1-2 days on a 68% confidence level from raw glow curve data, which improves upon prior methods that had 2-4 days uncertainty.
The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialprüfungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution. This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a glow curve deconvolution as input to a neural network.