Particle identification in the GlueX detector using a multi-layer perceptron

In particle physics experiments, identifying the types of particles registered in a detector is essential for the accurate reconstruction of particle collisions. At Thomas Jefferson National Accelerator Facility (Jefferson Lab), the GlueX experiment performs particle identification (PID) by setting specific thresholds, known as cuts, on the kinematic properties of tracks and showers obtained from detector hits. Our research aims to enhance this cut-based method by employing machine-learning algorithms based on multi-layer perceptrons. This approach offers an exciting opportunity to uncover underlying correlations among PID variables in the reconstructed kinematic data. Our study illustrates that an MLP can identify charged and neutral particles in Monte Carlo (MC) simulated GlueX data with significantly improved accuracy over the current cuts-based PID method.