Simulation and experimental comparison of the performance of four-corner-readout plastic scintillator muon-detector system

摘要: Cosmic-ray muons are highly penetrating background-radiation particles found in natural environments. In this study, we develop and test a plastic scintillator muon detector based on machine-learning algorithms. The detector underwent muon position-resolution tests at the Institute of Modern Physics in Lanzhou using a mul tiwire drift chamber (MWDC) experimental platform. In the simulation, the same structural and performance parameters were maintained to ensure the reliability of the simulation results. The Gaussian process regression (GPR) algorithm was used as the position-reconstruction algorithm owing to its optimal performance. The re sults of the Time Difference of Arrival algorithm were incorporated as one of the features of the GPR model to reconstruct the muon hit positions. The accuracy of the position reconstruction was evaluated by comparing the experimental results with Geant4 simulation results. In the simulation, large-area plastic scintillator detectors achieved a position resolution better than 20 mm. In the experimental-platform tests, the position resolutions of the test detectors were 27.9 mm. We also analyzed factors affecting the position resolution, including the crit ical angle of the total internal reflection of the photomultiplier tubes and distribution of muons in the MWDC. Simulations were performed to image both large objects and objects with different atomic numbers. The results showed that the system could image high- and low-Z materials in the constructed model and distinguish objects with significant density differences. This study demonstrates the feasibility of the proposed system, thereby providing a new detector system for muon-imaging applications.