No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry

TL;DR

This paper demonstrates that deep neural networks can accurately estimate the date of ionizing irradiation exposure from thermoluminescence glow curves, significantly improving prediction accuracy over previous methods.

Contribution

The study introduces a deep convolutional neural network that directly predicts irradiation dates from raw glow curve data, eliminating the need for glow curve deconvolution.

Findings

Irradiation date can be predicted within 1-2 days uncertainty.

Deep learning outperforms previous feature-based neural network approaches.

Method works on novel TL-DOS personal dosimeters.

Abstract

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 Materialpr\"ufungsamt 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.