Graph Embeddings for CEBAF Operations: Progress and Future Plans

Conference · Tue Nov 01 00:00:00 EDT 2022

DOI:https://doi.org/10.2172/1963703· OSTI ID:1963703

Tennant, C. ^[1]; Moser, Daniel ^[1]; Larrieu, Theodore ^[1]; Li, Jundong ^[2]; Wang, Song ^[2]; Xu, Zhiming ^[2]

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1. Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
2. UVA

We describe research towards leveraging deep learning on graph representations of the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF) in order to create a tool for improving the efficiency of beam tuning tasks. Specifically, we use graphs to represent the injector beamline at any arbitrary date and time and invoke a graph neural network to extract a low-dimensional, informative representation that can be visualized in two-dimensions. By analyzing years of operational data from the CEBAF archiver, good and bad regions of parameter space can be identified. The goal is to exercise this framework as a real-time tool to aid beam tuning, which represents the dominant source of machine downtime.

• Research Organization: Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Nuclear Physics (NP)
• DOE Contract Number: AC05-06OR23177
• OSTI ID: 1963703
• Report Number(s): JLAB-ACP-22-3783; DOE/OR/23177-5882
• Country of Publication: United States
• Language: English