ESS DTL Tuning Using Machine Learning Methods

J S Lundquist, S Werin, Natalia Milas, E Nilsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

The European Spallation Source, currently under construction in Lund, Sweden, will be the world's most powerful neutron source. It is driven by a proton linac with a current of 62.5 mA, 2.86 ms long pulses at 14 Hz. The final section of its normal-conducting front-end consists of a 39 m long drift tube linac (DTL) divided into five tanks, designed to accelerate the proton beam from 3.6 MeV to 90 MeV. The high beam current and power impose challenges to the design and tuning of the machine and the RF amplitude and phase have to be set within 1% and 1 ∘ of the design values. The usual method used to define the RF set-point is signature matching, which can be a time consuming and challenging process, and new techniques to meet the growing complexity of accelerator facilities are highly desirable. In this paper we study the usage of Machine Learning to determine the RF optimum amplitude and phase. The data from a simulated phase scan is fed into an artificial neural network in order to identify the needed changes to achieve the best tuning. Our test for the ESS DTL1 shows promising results, and further development of the method will be outlined.
Original languageEnglish
Title of host publicationProceedings of IPAC2021
PublisherJACoW Publishing
Pages1872-1875
Number of pages4
ISBN (Electronic)978-3-95450-214-1
DOIs
Publication statusPublished - 2021
Event12th International Particle Accelerator Conference, IPAC 2021 - , Brazil
Duration: 2021 May 24 → …

Publication series

NameInternational Particle Accelerator Conference
ISSN (Electronic)2673-5490

Conference

Conference12th International Particle Accelerator Conference, IPAC 2021
Country/TerritoryBrazil
Period2021/05/24 → …

Subject classification (UKÄ)

  • Accelerator Physics and Instrumentation

Free keywords

  • DTL
  • cavity
  • linac
  • network
  • proton

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