BOOSTR: A Dataset for Accelerator Control Systems
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Abstract (arXiv)
The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 2.
5 papers.
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- 2022FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning16 Jul 2022