Machine Learning based Particle Track Reconstruction for JLab Hall-A GEM Trackers

The Super BigBite Spectrometer (SBS) program of experiments aimed at nucleon form factor measurements is currently underway at Jefferson Lab. The program has yet to start its most demanding experiment, GEp-V, which is expected to encounter very high particle rates and backgrounds, reaching unprecedented levels compared to any other experiment in the world. The SBS program is using Gas Electron Multiplier (GEM) detectors for particle track reconstruction. The conventional tracking algorithms are likely unable to efficiently reconstruct tracks with these high data rates in the GEp experiment and other future high luminosity experiments, such as those in the Jefferson Lab SoLID project. A possible solution would be to employ a machine learning (ML) based approach. ML algorithms will be utilized to generate hit points along the detector planes using signal data from the GEM electronics. These candidate hit points will then be used to generate particle tracks by employing additional algorithms. The second part of the project is currently underway, involving the utilization of Graph Neural Networks (GNNs). The existing traditional algorithms are being used to develop supervised ML models, which will then be extended to incorporate standalone unsupervised learning models. The progress of this novel approach will be presented.