Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks
This provides a novel method for improving PMT accuracy in neutrino detection experiments, addressing a specific bottleneck in particle physics instrumentation.
The researchers tackled the problem of photomultiplier tube (PMT) saturation, which distorts linear response needed for photon counting and neutrino energy reconstruction, by using pulse-shape analysis and an artificial neural network to predict and restore the ideal number of photoelectrons, enabling in-situ estimation of linearity range.
The linear response of a photomultiplier tube (PMT) is a required property for photon counting and reconstruction of the neutrino energy. The linearity valid region and the saturation response of PMT were investigated using a linear-alkyl-benzene (LAB)-based liquid scintillator. A correlation was observed between the two different saturation responses, with pulse-shape distortion and pulse-area decrease. The observed pulse-shape provides useful information for the estimation of the linearity region relative to the pulse-area. This correlation-based diagnosis allows an ${in}$-${situ}$ estimation of the linearity range, which was previously challenging. The measured correlation between the two saturation responses was employed to train an artificial-neural-network (ANN) to predict the decrease in pulse-area from the observed pulse-shape. The ANN-predicted pulse-area decrease enables the prediction of the ideal number of photoelectrons irrelevant to the saturation behavior. This pulse-shape-based machine learning technique offers a novel method for restoring the saturation response of PMTs.