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Title Experimental Demonstration of Machine Learning Application in LHC Optics Commissioning
  • E. Fol, F.S. Carlier, J. Dilly, M. Hofer, J. Keintzel, M. Le Garrec, E.H. Maclean, T.H.B. Persson, F. Soubelet, R. Tomás García, A. Wegscheider
    CERN, Meyrin, Switzerland
  • J.F. Cardona
    UNAL, Bogota D.C, Colombia
Abstract Recently, we conducted successful studies on the suitability of machine learning (ML) methods for optics measurements and corrections, incorporating novel ML-based methods for local optics corrections and reconstruction of optics functions. After performing extensive verifications on simulations and past measurement data, the newly developed techniques became operational in the LHC commissioning 2022. We present the experimental results obtained with the ML-based methods and discuss future improvements. Besides, we also report on improving the Beam Position Monitor (BPM) diagnostics with the help of the anomaly detection technique capable to identify malfunctioning BPMs along with their possible fault causes.
Paper download MOPOPT047.PDF [0.256 MB / 4 pages]
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Conference IPAC2022
Series International Particle Accelerator Conference (13th)
Location Bangkok, Thailand
Date 12-17 June 2022
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Frank Zimmermann (CERN, Meyrin, Switzerland); Hitoshi Tanaka (RIKEN, Hyogo, Japan); Porntip Sudmuang (SRLI, Nakhon, Thailand); Prapong Klysubun (SRLI, Nakhon, Thailand); Prapaiwan Sunwong (SRLI, Nakhon, Thailand); Thakonwat Chanwattana (SRLI, Nakhon, Thailand); Christine Petit-Jean-Genaz (CERN, Meyrin, Switzerland); Volker R.W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-227-1
Online ISSN 2673-5490
Received 07 June 2022
Accepted 16 June 2022
Issue Date 06 July 2022
DOI doi:10.18429/JACoW-IPAC2022-MOPOPT047
Pages 359-362
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