Machine learning methods for subpixel trajectory reconstruction in discretized position detectors
arXiv:2512.20645
The paper evaluates machine‑learning approaches, especially transformer neural networks, for reconstructing subpixel particle trajectories in discretized scintillator detector arrays, showing they outperform traditional centroid methods in position and angular resolution using simulated muon data.
Abstract
In this study, we demonstrate that compared with traditional centroid-based methods, machine learning methods (particularly transformer-based architectures) achieve superior subpixel position and therefore angular resolution in discretized particle detectors. Using Geant4 Monte Carlo simulated cosmic ray muon data from an 8x8 segmented scintillator detector array, we compare four reconstruction approaches: transformer neural networks, convolutional neural networks, linear regression, and energy-weighted centroids. The transformer architecture achieves the best angular reconstruction with a root mean square error of 1.14° and a position mean absolute error of 0.24 cm, representing improvements of 2.22x and 6.33x, respectively, over the centroid method. These results enable precise particle trajectory reconstruction for applications in muon tomography and cosmic ray detection.