Simulation study on multi-unit integrated muon detection system based on recurrent neural network algorithm

Abstract To achieve imaging of objects using cosmic ray muons, it is essential to determine the muon trajectories, and accurately locating the muon hit points is key to trajectory reconstruction. Current mainstream muon track detection systems require multiple electronic channels to precisely pinpoint the muon hit points, resulting in complex structures and high costs. The aim of this study is to design a muon track detection system characterized by simplicity, low cost, and high precision. This system is based on Geant4 software and features a circular plastic scintillator without segmentation coupled with Silicon Photon Multipliers (SiPMs). The simulation involves the use of SiPMs to collect the number of photons and the time of triggering SiPM responses as characteristic parameters, and employs a recurrent neural network (RNN) algorithm as the method for muon localization. The simulation results show that a plastic scintillator with a diameter of 20 cm and a thickness of 10 mm can achieve a position resolution of less than 1 cm while ensuring a large detection area. The more SiPMs are coupled, the more accurate the collected photon information is, which improves the position resolution of the detector. The reconstruction accuracy of the Gated Recurrent Unit (GRU) algorithm is comparable to that of the Long Short-Term Memory (LSTM) algorithm within the uncertainties, but the reconstruction speed of the GRU algorithm is better than that of the LSTM algorithm. Under the same conditions, the training speed of the GRU algorithm is about 9/11 of that of the LSTM algorithm. The results of the multi-layer detection unit optimization scheme proposed in this study indicate that as the minimum number of detection unit layers in the muon positioning module increases, the position resolution of the system gradually improves. Considering the cost, the GRU algorithm can be chosen as the reconstruction algorithm, and the minimum detection unit with 8 SiPMs coupled in two layers can be selected as the system optimization scheme. Considering the accuracy of position resolution, the LSTM algorithm can be chosen as the reconstruction algorithm, and the minimum detection unit with 6 SiPMs coupled in three layers can be selected as the system optimization scheme. The multi-unit integrated muon detection system proposed in this study ensures a large detection area while enabling the simplest minimum detection units in a multilayer structure to work in coordination, thereby optimizing the position resolution of the entire muon detection system.