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| WEB03 |
Application of Machine Learning to Beam Diagnostics |
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- E. Fol, R. Tomás
CERN, Geneva, Switzerland
- J.M. Coello de Portugal
PSI, Villigen PSI, Switzerland
- G. Franchetti
GSI, Darmstadt, Germany
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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.
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Slides WEB03 [5.721 MB]
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| DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-FEL2019-WEB03
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| About • |
paper received ※ 20 August 2019 paper accepted ※ 27 August 2019 issue date ※ 05 November 2019 |
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