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BOOSTR: A Dataset for Accelerator Control Systems

by *,† and
Fermi National Accelerator Laboratory, Batavia, IL 60510, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Vladimir Sreckovic
Received: 30 January 2021 / Revised: 1 April 2021 / Accepted: 13 April 2021 / Published: 16 April 2021
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. View Full-Text
Keywords: dataset; artificial intelligence; machine learning; accelerator control systems; anomaly detection dataset; artificial intelligence; machine learning; accelerator control systems; anomaly detection
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MDPI and ACS Style

Kafkes, D.; St. John, J. BOOSTR: A Dataset for Accelerator Control Systems. Data 2021, 6, 42. https://0-doi-org.brum.beds.ac.uk/10.3390/data6040042

AMA Style

Kafkes D, St. John J. BOOSTR: A Dataset for Accelerator Control Systems. Data. 2021; 6(4):42. https://0-doi-org.brum.beds.ac.uk/10.3390/data6040042

Chicago/Turabian Style

Kafkes, Diana; St. John, Jason. 2021. "BOOSTR: A Dataset for Accelerator Control Systems" Data 6, no. 4: 42. https://0-doi-org.brum.beds.ac.uk/10.3390/data6040042

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