Exploration of deep-learning-based dose rate estimation model for a large-volume plastic scintillation detector

This study explores a deep-learning-based dose rate estimation model for a large-volume plastic scintillation detector. Large-volume plastic scintillators are typically utilized as detector materials for radiation portal monitors (RPMs); however, they can also be utilized for environmental radiation monitoring. We introduced a deep-learning approach to estimate the ambient dose equivalent (H*(10)) from the spectral input using a transformer architecture. The performance of the model was compared with that of other models with conventional architectures and verified using simulated and measured spectra. In addition, we present a model explanation and an uncertainty quantification approach to ensure the reliability of the dose rate estimation model. Our verification demonstrates that the behavior of the model aligns with domain knowledge and that the uncertainty of the model's estimations increases according to the increment in the statistical uncertainties of the input spectra. The experimental results indicate that the model shows a promising performance as a practical application for RPMs.