scholarly journals Particle tracking study of the injection process with field interferences in a rapid cycling synchrotron

2008 ◽  
Vol 11 (6) ◽  
Author(s):  
M. J. Shirakata ◽  
H. Fujimori ◽  
Y. Irie ◽  
T. Takayanagi
Keyword(s):  
Particle Tracking ◽  
Rapid Cycling ◽  
Injection Process ◽  
Rapid Cycling Synchrotron

scholarly journals Tune Control Improvements on the Rapid Cycling Synchrotron

1981 ◽  
Vol 28 (3) ◽  
pp. 3020-3022 ◽  
Author(s):  
C. Potts ◽  
M. Faber ◽  
G. Gunderson ◽  
M. Knott ◽  
D. Voss
Keyword(s):  
Rapid Cycling ◽  
Rapid Cycling Synchrotron

Radiation Shielding of a Beta-Beam Rapid Cycling Synchrotron

2009 ◽  
Vol 168 (2) ◽  
pp. 462-466
Author(s):  
Stefania Trovati ◽  
Matteo Magistris ◽  
Marco Silari
Keyword(s):  
Radiation Shielding ◽  
Rapid Cycling ◽  
Rapid Cycling Synchrotron ◽  
Beta Beam

Study of the RF Capture Process in the Argonne Rapid Cycling Synchrotron

1981 ◽  
Vol 28 (3) ◽  
pp. 3040-3042 ◽  
Author(s):  
Yanglai Cho ◽  
Charles W. Potts ◽  
Antanas V. Rauchas ◽  
Seung-Ai Shin ◽  
Harunori Takeda
Keyword(s):  
Rapid Cycling ◽  
Capture Process ◽  
Rapid Cycling Synchrotron

scholarly journals BOOSTR: A Dataset for Accelerator Control Systems

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 42
Author(s):  
Diana Kafkes ◽  
Jason St. John
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Reinforcement Learning ◽  
Control Systems ◽  
Power Supply ◽  
Cycle Time ◽  
Rapid Cycling ◽  
Operation Optimization ◽  
Advanced Control ◽  
Rapid Cycling Synchrotron

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.

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