Document Type

Article

Publication Date

2025

DOI

10.1088/2632-2153/ae02df

Publication Title

Machine Learning: Science and Technology

Volume

6

Issue

3

Pages

035049 (1-15)

Abstract

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

Rights

© 2025 The Authors

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data cannot be made publicly available upon publication due to the legal restrictions preventing unrestricted public distribution. The data that support the findings of this study are available upon reasonable request from the authors."

Original Publication Citation

Mohammed, A. H., Rajput, K., Taylor, S., Furletov, D., Furletov, S., & Schram, M. (2025). Geometric GNNs for charged particle tracking at GlueX. Machine Learning: Science and Technology, 6(3), Article 035049. https://doi.org/10.1088/2632-2153/ae02df

ORCID

0000-0002-3475-2871 (Schram)

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