Keyword: controls
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WEVIR10 Adaptive Feedback Control and Machine Learning for Particle Accelerators FEL, electron, diagnostics, feedback 53
 
  • A. Scheinker
    LANL, Los Alamos, New Mexico, USA
 
  The precise control of charged particle beams, such as an electron beam’s longitudinal phase space as well as the maximization of the output power of a free electron laser (FEL), or the minimization of beam loss in accelerators, are challenging tasks. For example, even when all FEL parameter set points are held constant both the beam phase space and the output power have high variance because of the uncertainty and time-variation of thousands of coupled parameters and of the electron distribution coming off of the photo cathode. Similarly, all large accelerators face challenges due to time variation, leading to beam losses and changing behavior even when all accelerator parameters are held fixed. We present recent efforts towards developing machine learning methods along with automatic, model-independent feedback for automatic tuning of charge particle beams in particle accelerators. We present experimental results from the LANSCE linear accelerator at LANL, the EuXFEL, AWAKE at CERN, FACET-II and the LCLS.  
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2020-WEVIR10  
About • paper received ※ 27 May 2020       paper accepted ※ 12 June 2020       issue date ※ 14 June 2020  
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WEVIR11 Safety System for the Respect of Nuclear Requirements of SPIRAL2 Facility experiment, operation, linac, ISOL 57
 
  • P. Anger, V.C. Cingal, JC-P. Pacary, S.P.G. Perret-Gatel, A. Savalle
    GANIL, Caen, France
 
  The SPIRAL2 Facility at GANIL is based on the construction of a superconducting ion CW LINAC (up to 5 mA - 40 MeV deuteron beams and up to 1 mA - 14.5 MeV/u heavy ion beams) with 2 experimental areas called S3 and NFS. For safety classified systems, SPIRAL2 project system engineering sets up a specific reinforced process, based on V-Model, to validate, at each step, all the requirements (technical, nuclear safety, quality, reliability, interfaces…) from the functional specifications to the final validation. Since 2016, safety devices have been under construction and in test phase. These tests which are pre-requisites to deliver the first beam demonstrated that both functional and safety requirements are fulfilled. Currently, all of them are in operation for the LINAC and NFS commissioning phases. This contribution will describe the requirements, the methodology, the quality processes, the technical studies, the failure mode and effects analysis, the tests, the status and will propose you a feedback.  
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2020-WEVIR11  
About • paper received ※ 01 June 2020       paper accepted ※ 14 June 2020       issue date ※ 15 June 2020  
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WEVIR12 Machine Learning Techniques for Optics Measurements and Corrections optics, quadrupole, simulation, network 61
 
  • E. Fol, R. Tomás García
    CERN, Meyrin, Switzerland
  • G. Franchetti
    GSI, Darmstadt, Germany
 
  Recently, various efforts have presented Machine Learning (ML) as a powerful tool for solving accelerator problems. In the LHC a decision tree-based algorithm has been applied to detect erroneous beam position monitors demonstrating successful results in operation. Supervised regression models trained on simulations of LHC optics with quadrupole errors promise to significantly speed-up optics corrections by finding local errors in the interaction regions. The implementation details, results and future plans for these studies will be discussed following a brief introduction to ML concepts and its suitability to different problems in the domain of accelerator physics.  
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2020-WEVIR12  
About • paper received ※ 02 June 2020       paper accepted ※ 12 June 2020       issue date ※ 16 June 2020  
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