利用脉冲形状和人工神经网络恢复 PMT 的饱和响应
Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks
AI总结：
本研究利用 LAB 液体闪烁体发现 PMT 脉冲形状畸变与脉冲面积减小的相关性，并训练 ANN 由脉冲形状预测饱和导致的面积损失，从而恢复理想光电子数与线性响应。
摘要：
光电倍增管（PMT）的线性响应是光子计数和中微子能量重建所需的特性。研究人员使用基于线性烷基苯（LAB）的液体闪烁体，研究了 PMT 的线性有效区域和饱和响应。实验观察到两种不同饱和响应之间存在相关性，表现为脉冲形状畸变和脉冲面积减小。所观察到的脉冲形状为相对于脉冲面积估算线性区域提供了有用信息。这种基于相关性的诊断能够实现对线性范围的原位（in-situ）估算，而这在以往具有挑战性。研究利用测得的两种饱和响应之间的相关性训练人工神经网络（ANN），根据观察到的脉冲形状预测脉冲面积的减小量。ANN 预测的脉冲面积减小量使得预测与饱和行为无关的理想光电子数成为可能。这种基于脉冲形状的机器学习技术为恢复 PMT 的饱和响应提供了一种新方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
