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https://doi.org/10.18429/JACoW-IPAC2020-WEVIR12
Title Machine Learning Techniques for Optics Measurements and Corrections
Authors
  • E. Fol, R. Tomás García
    CERN, Meyrin, Switzerland
  • G. Franchetti
    GSI, Darmstadt, Germany
Abstract 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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Conference IPAC2020
Series International Particle Accelerator Conference (11th)
Location Caen, France
Date 10-15 May 2020
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Mike Seidel (PSI, Villigen, Switzerland); Ralph W. Aßmann (DESY, Hamburg, Germany); Frédéric Chautard (GANIL, Caen. France); Volker R.W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-213-4
Online ISSN 2673-5490
Received 02 June 2020
Accepted 12 June 2020
Issue Date 10 October 2020
DOI doi:10.18429/JACoW-IPAC2020-WEVIR12
Pages 61-66
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.