Enhancement of beta spectrometry using double scintillators and ML-based unfolding

We present a novel beta spectrometer that consists of two identical plastic scintillators with one scintillator screened by a thin copper plate as a beta shield. The screened scintillator responds only to gamma photons while the other scintillator responds to both beta particles and gamma photons. The spectrometer’s response to beta and gamma radiations was characterized by experiments and Monte Carlo simulations. The gamma responses of the scintillators were in good agreement in most energy region while the screened scintillator showed a notable gamma attenuation in the low energy region below 150 keV. Comparison of the simulated and measured pulse height spectra showed good agreements for both beta and gamma radiations. For beta spectrum analysis, a simple gamma subtraction method and a convolutional neural network (CNN)-based method were investigated for various mixed beta–gamma fields. The subtraction method showed good accuracy in most energy regions while a notable overestimation of beta fluence was observed in the low energy region, which was caused by the gamma attenuation effect of the screened scintillator. The outcomes of the CNN method showed good agreements with the true beta fluence spectra for the validation dataset, however, the CNN model led to a significant overestimation for a dataset produced using the radionuclides that have not been used in the training datasets. To take the advantages of the outperforming features of both unfolding methods, a hybrid algorithm was deduced by applying a tolerance range to the subtraction result. • A beta spectrometer consisting of two identical scintillators and a thin Cu plate. • A hybrid unfolding combines a ML algorithm and a simple gamma subtraction method. • Linear combination of measured and simulated data to produce dataset for ML-unfolding. • Accurate beta fluence unfolding result is achieved in the energy region of interests.