Author: Di Castro, M.
Paper Title Page
TUPOTK061 Prospects to Apply Machine Learning to Optimize the Operation of the Crystal Collimation System at the LHC 1362
 
  • 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
 
  Funding: Research supported by the HL-LHC project.
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.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOTK061  
About • Received ※ 07 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 21 June 2022 — Issue date ※ 24 June 2022
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WEPOST013 Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning 1707
 
  • F.M. Velotti, M. Di Castro, L.S. Esposito, M.A. Fraser, S.S. Gilardoni, B. Goddard, V. Kain, E. Matheson
    CERN, Meyrin, Switzerland
 
  The CERN Super Proton Synchrotron (SPS) routinely delivers proton and heavy ion beams to the North experimental Area (NA) in the form of 4.8 s spills. To produce such a long flux of particles, resonant third integer slow extraction is used, which, by design, foresees primary beam lost on the electrostatic septum wires to separate circulating from extracted beam. Shadowing with thin bent crystal has been proposed and successfully tested in the SPS, as detailed in *. In 2021, a thin crystal was used for physics production showing results compatible with what measured during early testing. In this paper, the results from the 2021 physics run are presented also comparing particle losses at extraction with previous operational years. The setting up of the crystal using numerical optimisers is detailed, with possible implementation of reinforcement learning (RL) agents to improve the setting up time. Finally, the full exploitation of crystal shadowing via multi-array crystals is discussed, together with the performance reach in the SPS.
F.Velotti, et. al, "Septum shadowing by means of a bent crystal to reduce slow extraction beam loss", Phys. Rev. Accel. Beams 22, 093502 - Published 27 September 2019
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOST013  
About • Received ※ 06 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 02 July 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)