Graph Neural Networks for High Luminosity Track Reconstruction

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b" With the upgrade to HL-LHC, traditional algorithms in the event analysis pipeline may struggle to scale to meet throughput requirements, due to the density of detector data and incompatibility with modern heterogeneous parallelism. A promising alternative path is emerging, by treating detector data as a graph-like structure and applying Graph Neural Networks (GNNs) to learn a representation of the underlying physics. Subsequently, there has been a flurry of progress in understanding which GNN architectures are best suited to which stage of analysis - track reconstruction, jet tagging, seeding, event generation, and end-to-end analysis. \r\n\r\n While GNNs clearly bring a new way of handling HEP analysis, they can be cumbersome and expensive, and technologies are still being developed to handle them effectively. The Exatrkx Project is a collective effort across 10 institutions to study and validate ML approaches to tracking in HEP experiments, including ATLAS and DUNE. I will discuss how we are bringing the speed and accuracy of GNNs to these challenging datasets. In particular, highly GPU-optimized graph construction from O(100k)-sized point clouds and graph manipulation libraries are combined with state-of-the-art distributed ML training and inference techniques to deliver sub-second track reconstruction on high-luminosity datasets. This talk will cover many of these ideas, as well as ways that symmetry and representation-learning are included in the GNN models, and the progress being made on integrating graph techniques with existing tracking frameworks, like ACTS. \r\n \r\n\r\n Daniel Murnane is a postdoctoral researcher at Lawrence Berkeley Lab. He is currently working with the Exatrkx project, a collaboration of US institutions that develops AI/ML techniques for track reconstruction, targeting exascale computing resources. Daniel's research focuses on graph neural networks (GNNs) for high energy physics problems, including building physics-informed &…