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Title Detection and Classification of Collective Beam Behaviour in the LHC
  • L. Coyle, F. Blanc, T. Pieloni, M. Schenk
    EPFL, Lausanne, Switzerland
  • X. Buffat, M. Solfaroli Camillocci, J. Wenninger
    CERN, Meyrin, Switzerland
  • E. Krymova, G. Obozinski
    SDSC, Lausanne, Switzerland
Abstract Collective instabilities can lead to a severe deterioration of beam quality, in terms of reduced beam intensity and increased beam emittance, and consequently a reduction of the collider’s luminosity. It is therefore crucial for the operation of the CERN’s Large Hadron Collider to understand the conditions in which they appear in order to find appropriate mitigation measures. Using bunch-by-bunch and turn-by-turn beam amplitude data, courtesy of the transverse damper’s observation box (ObsBox), a novel machine learning based approach is developed to both detect and classify these instabilities. By training an autoencoder neural network on the ObsBox amplitude data and using the model’s reconstruction error, instabilities and other phenomena are separated from nominal beam behaviour. Additionally, the latent space encoding of this autoencoder offers a unique image like representation of the beam amplitude signal. Leveraging this latent space representation allows us to cluster the various types of anomalous signals.
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Conference IPAC2021
Series International Particle Accelerator Conference (12th)
Location Campinas, SP, Brazil
Date 24-28 May 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-214-1
Online ISSN 2673-5490
Received 19 May 2021
Accepted 19 July 2021
Issue Date 27 August 2021
DOI doi:10.18429/JACoW-IPAC2021-THPAB260
Pages 4318-4321
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.