Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks
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 - 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.
Keywords
Cite
@article{arxiv.2302.06170,
title = {Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks},
author = {Hyun-Gi Lee and Jungsic Park},
journal= {arXiv preprint arXiv:2302.06170},
year = {2023}
}
Comments
10 pages, 8 figures and appendix