Automatic energy calibration of radiation portal monitors using deep learning and spectral remapping

Abstract

Radiation portal monitors comprising large-volume plastic scintillators are commonly used to monitor the smuggling of radioactive materials. Various applications have been proposed to perform radioisotope identification using these monitors. Such applications require calibration of the spectrum measured by the detector to obtain the physical energy spectrum. The relationship between the multichannel readout and energy bins depends on environmental conditions: it implies that energy calibration in radiation portal monitors should be performed periodically, even multiple times in a single day, thus demanding for a simple and fast energy calibration method. In this study, a deep learning model and a spectral remapping method were used to transform the raw detector output into an energy spectrum with constant energy bins. The deep learning model was designed to predict energy calibration parameters based on the channel spectrum of a single radioisotope. The dataset used to train the deep learning model was generated using the spectrum of the radiation portal monitor. A convolutional neural network model was utilized to evaluate the performance. The remapping method was designed to remap calibrated energy bins to fixed energy bins based on the linear interpolation of nearby bins. The performance of the neural network model and of the remapping method were then evaluated based on several measured spectra taken with different conditions, and found to be adequate to fulfill the requirements.