Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks

TL;DR

This paper presents a novel machine learning approach using pulse-shape analysis and neural networks to restore the linear response of photomultiplier tubes, improving photon counting accuracy in neutrino detection.

Contribution

The study introduces a new pulse-shape-based neural network method for estimating and correcting PMT saturation effects in situ.

Findings

Neural network accurately predicts pulse-area decrease from pulse-shape.

Correlation between pulse-shape distortion and saturation response established.

Method enables more accurate photon counting in neutrino experiments.

Abstract

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.