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'Journal of Accelerator Conferences Website' (JACoW) is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.

https://doi.org/10.18429/JACoW-HB2021-TUEC4
Title Test of Machine Learning at the CERN LINAC4
Authors
  • 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
Abstract 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.
Paper download TUEC4.PDF [0.358 MB / 5 pages]
Slides download TUEC4_TALK.PDF [2.879 MB]
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Conference HB2021
Series ICFA ABDW on High-Intensity and High-Brightness Hadron Beams (64th)
Location Batavia, IL, USA
Date 04-08 October 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Eliana Gianfelice (FNAL, Batavia, IL, USA); Sergei Nagaitsev (FNAL, Batavia, IL, USA); Dong Eon Kim (PAL, Pohang, South Korea); Michaela Marx (DESY, Hamburg, Germany); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-225-7
Online ISSN 2673-5571
Received 07 October 2021
Revised 21 October 2021
Accepted 23 November 2021
Issue Date 19 December 2021
DOI doi:10.18429/JACoW-HB2021-TUEC4
Pages 181-185
Copyright
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.