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Title Prospects to Apply Machine Learning to Optimize the Operation of the Crystal Collimation System at the LHC
  • M. D’Andrea, G. Azzopardi, M. Di Castro, E. Matheson, D. Mirarchi, S. Redaelli, G. Valentino
    CERN, Meyrin, Switzerland
  • G. Ricci
    Sapienza University of Rome, Rome, Italy
Abstract Crystal collimation relies on the use of bent crystals to coherently deflect halo particles onto dedicated collimator absorbers. This scheme is planned to be used at the LHC to improve the betatron cleaning efficiency with high-intensity ion beams. Only particles with impinging angles below 2.5 urad relative to the crystalline planes can be efficiently channeled at the LHC nominal top energy of 7 Z TeV. For this reason, crystals must be kept in optimal alignment with respect to the circulating beam envelope to maximize the efficiency of the channeling process. Given the small angular acceptance, achieving optimal channeling conditions is particularly challenging. Furthermore, the different phases of the LHC operational cycle involve important dynamic changes of the local orbit and optics, requiring an optimized control of position and angle of the crystals relative to the beam. To this end, the possibility to apply machine learning to the alignment of the crystals, in a dedicated setup and in standard operation, is considered. In this paper, possible solutions for automatic adaptation to the changing beam parameters are highlighted and plans for the LHC ion runs starting in 2022 are discussed.
Funding Research supported by the HL-LHC project.
Paper download TUPOTK061.PDF [0.248 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
Revised 12 June 2022
Accepted 21 June 2022
Issue Date 24 June 2022
DOI doi:10.18429/JACoW-IPAC2022-TUPOTK061
Pages 1362-1365
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.