X. Buffat, S.V. Furuseth, G. Vicentini
CERN, Geneva, Switzerland
S.V. Furuseth
EPFL, Lausanne, Switzerland
Control of chromaticity is often critical to mitigate collective instabilities in high energy synchrotrons, yet classical measurement methods are of limited use during high intensity operation. We explore the possibility to extract this information from beam transfer function measurements, with the development of a theoretical background that includes the impact of wakefields and by analysis of multi-particle tracking simulations. The investigations show promising results that could improve the operation of the HL-LHC by increasing stability margins.
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V. Kain, N. Madysa, P.K. Skowroński, I. Vojskovic
CERN, Geneva, Switzerland
N. Bruchon
University of Trieste, Trieste, Italy
S. Hirlaender, G. Valentino
University of Malta, Information and Communication Technology, Msida, Malta
The CERN H⁻ linear accelerator, LINAC4, served as a test bed for advanced algorithms during the CERN Long Shutdown 2 in the years 2019/20. One of the main goals was to show that reinforcement learning with all its benefits can be used as a replacement for numerical optimization and as a complement to classical control in the accelerator control context. Many of the algorithms used were prepared beforehand at the electron line of the AWAKE facility to make the best use of the limited time available at LINAC4. An overview of the algorithms and concepts tested at LINAC4 and AWAKE will be given and the results discussed.
