Speaker
Aayush Bhattarai
Description
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 trajectories in the \textit{EMPHATIC} table-top spectrometer using simulated data. The model takes raw hit information from the silicon strip detectors (SSDs) as input and is trained using simulations to predict momentum components and the scattering angle of the particle. We describe the GNN architecture, training procedure, and performance metrics.