BOOSTR: A Dataset for Accelerator Control Systems

Diana Kafkes and Jason St. John
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Diana Kafkes: Fermi National Accelerator Laboratory, Batavia, IL 60510, USA
Jason St. John: Fermi National Accelerator Laboratory, Batavia, IL 60510, USA

Data, 2021, vol. 6, issue 4, 1-11

Abstract: 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.

Keywords: dataset; artificial intelligence; machine learning; accelerator control systems; anomaly detection (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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