Track reconstruction using graph neural networks in the EMPHATIC experiment

Track reconstruction is essential for extracting physics observables from detector data in high-energy and nuclear physics experiments. In this work, we investigate the use of graph neural networks (GNNs) to reconstruct particle momenta in the EMPHATIC tabletop spectrometer using simulated data. The model takes raw hit information from the silicon strip detectors (SSDs) as input and is trained to measure momentum components and the scattering angle of the particle. We describe the GNN architecture, training procedure, and performance metrics and present preliminary resolution estimates for momentum components and scattering angle. These results demonstrate the potential of GNN-based approaches in track reconstruction tasks within complex detector environments like EMPHATIC.