Author: Dunn, G.M.
Paper Title Page
TH1I2 Reinforcement Learning and Bayesian Optimization for Ion Linac Operations 130
 
  • J.L. Martinez Marin, B.R. Blomberg, K.J. Bunnell, G.M. Dunn, E. Letcher, B. Mustapha, D. Stanton
    ANL, Lemont, Illinois, USA
 
  Funding: This work was supported by the U.S. Department of Energy, under Contract No. DE-AC02-06CH11357. This research used the ATLAS facility, which is a DOE Office of Nuclear Physics User Facility.
The use of artificial intelligence can significantly reduce the time needed to tune an accelerator system such as the Argonne Tandem Linear Accelerator System (ATLAS) where a new beam is tuned once or twice a week. After establishing automatic data collection procedures and having analysed the data, machine learning models were developed and tested to tune subsections of the linac. Models based on Reinforcement Learning (RL) and Bayesian Optimization (BO) were developed, their respec-tive results are discussed and compared. RL and BO are well known AI techniques, often used for control systems. The results were obtained for a subsection of ATLAS that contains complex elements such as the radio-frequency quadrupole (RFQ). The models will be later generalized to the whole ATLAS linac, and similar models can be devel-oped for any accelerator with a modern control system.
 
slides icon Slides TH1I2 [4.617 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-HIAT2022-TH1I2  
About • Received ※ 09 July 2022 — Revised ※ 10 August 2022 — Accepted ※ 19 September 2022 — Issue date ※ 19 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)