Machine learning methods for subpixel trajectory reconstruction in discretized position detectors

TL;DR

This paper demonstrates that transformer-based machine learning methods outperform traditional centroid-based techniques in subpixel trajectory reconstruction, significantly improving angular and position resolution in discretized particle detectors.

Contribution

It introduces transformer neural networks for particle trajectory reconstruction, showing superior accuracy over existing methods using simulated cosmic ray muon data.

Findings

Transformer models achieve the lowest angular error of 1.14°

Position mean absolute error is reduced to 0.24 cm

Transformer methods outperform centroid-based approaches by over 6 times

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{\deg} 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.