Muon cosmic rays are highly penetrating background radiation present in the natural environment.Currently,the mainstream muon detectors include gas detectors and plastic scintillator strip detectors,known for their high precision but associated with high costs and complex structures.This study is based on the existing four-corner readout plastic scintillator detector in the laboratory.Through the application of the Long Short-Term Memory(LSTM)algorithm and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm,and utilizing the Geant4 simulation software,we simulated the construction of a large-area(800 mm x 800 mm)four-corner readout plastic scintillator muon imaging system for position reconstruction and imaging results.Firstly,we employed the LSTM algorithm as a method for muon localization on the detector.Through comparing the reconstructed incident position image results with those obtained from a 400 mmx400 mm detector in experiments,the reliability of the simulation methodology in this study was demonstrated.The position reconstruction results indicate that the simulated position resolution can reach the centimeter level.Secondly,we utilized the DBSCAN algorithm to perform clustering optimization on the PoCA scatter imaging results of"U""S"and"C"shaped models constructed with tungsten blocks.This step achieved a clear distinction between imaging points and noise points related to the tungsten block,enhancing the precision of the imaging results.This provides new insights and directions for the construction of muon detector systems.
关键词
蒙特卡罗模拟/Geant4/缪子成像/机器学习
Key words
Monte Carlo simulation/Geant4/muon imaging/machine learning
维普
万方数据