Author: Franchetti, G.
Paper Title Page
WEB03 Application of Machine Learning to Beam Diagnostics 311
  • E. Fol, R. Tomás
    CERN, Geneva, Switzerland
  • J.M. Coello de Portugal
    PSI, Villigen PSI, Switzerland
  • G. Franchetti
    GSI, Darmstadt, Germany
  Machine Learning (ML) techniques are widely used in science and industry to discover relevant information and make predictions from data. The application ranges from face recognition to High Energy Physics experiments. Recently, the application of ML has grown also in accelerator physics and in particular in the domain of diagnostics and control. The target is to provide an overview of ML techniques and to indicate beam diagnostics tasks where ML based solutions can be efficiently applied to complement or potentially surpass existing methods. Besides, a short summary of recent works will be given demonstrating the great interest for use of ML concepts in beam diagnostics and latest results of incorporating these concepts into accelerator problems, with the focus on beam optics related application.  
slides icon Slides WEB03 [5.721 MB]  
DOI • reference for this paper ※  
About • paper received ※ 20 August 2019       paper accepted ※ 27 August 2019       issue date ※ 05 November 2019  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)