Graph Analytics for CEBAF Operations
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
- Univ. of Virginia, Charlottesville, VA (United States)
We report on the progress achieved during a 2-year Laboratory Directed Research and Development (LDRD) project titled “Graph Analytics for CEBAF Operations”. The objective of this project is to leverage deep learning on graph representations of CEBAF’s injector beamline 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 (GNN) 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 (TJNAF), Newport News, VA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC05-06OR23177
- OSTI ID:
- 2331262
- Report Number(s):
- 2024-LDRD--2301
- Country of Publication:
- United States
- Language:
- English
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