Speaker
Description
Charged-particle track reconstruction is a central component of nuclear physics experiments, providing the foundation for identifying and analyzing particles produced in high-energy interactions. While traditional techniques—such as pattern-recognition algorithms and Kalman-filter–based tracking—have long been the standard, modern machine learning (ML) methods are increasingly addressing the challenges posed by complex detector geometries, high occupancies, and significant noise. Neural networks, graph neural networks (GNNs), and recurrent architectures have demonstrated improved accuracy, robustness, and scalability by learning directly from simulated and experimental data. These models can classify and select track candidates, resolve ambiguities from overlapping or missing hits, and predict full particle trajectories, all with the potential to operate in near-real-time. As computational capabilities advance, ML-driven tracking is becoming a transformative component of large-scale experiments, from the LHC to Jefferson Lab.
In this talk, we present recent progress in AI-enhanced charged-track identification within the CLAS12 detector, where machine-learning methods deliver significant gains in usable statistics over conventional reconstruction. We also demonstrate real-time event-reconstruction capabilities, including fast inference of particle momentum, direction, and species identification at data-acquisition speeds. These developments enable physics observables to be extracted directly from the experiment in real time, opening new paths toward high-precision and high-throughput nuclear science.