  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.
