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https://doi.org/10.18429/JACoW-IPAC2021-TUPAB298
Title First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT
Authors
  • A. Eichler, F. Burkart, J. Kaiser, W. Kuropka, O. Stein
    DESY, Hamburg, Germany
  • E. Bründermann, A. Santamaria Garcia, C. Xu
    KIT, Karlsruhe, Germany
Abstract Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE.
Funding Helmholtz Artificial Cooperation Unit
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Conference IPAC2021
Series International Particle Accelerator Conference (12th)
Location Campinas, SP, Brazil
Date 24-28 May 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-214-1
Online ISSN 2673-5490
Received 19 May 2021
Accepted 02 August 2021
Issue Date 17 August 2021
DOI doi:10.18429/JACoW-IPAC2021-TUPAB298
Pages 2182-2185
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.