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Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning

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 electro...

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Autores principales: Velotti, Francesco, Di Castro, Mario, Esposito, Luigi Salvatore, Fraser, Matthew, Gilardoni, Simone, Goddard, Brennan, Kain, Verena, Matheson, Eloise
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2022-WEPOST013
http://cds.cern.ch/record/2845830
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author Velotti, Francesco
Di Castro, Mario
Esposito, Luigi Salvatore
Fraser, Matthew
Gilardoni, Simone
Goddard, Brennan
Kain, Verena
Matheson, Eloise
author_facet Velotti, Francesco
Di Castro, Mario
Esposito, Luigi Salvatore
Fraser, Matthew
Gilardoni, Simone
Goddard, Brennan
Kain, Verena
Matheson, Eloise
author_sort Velotti, Francesco
collection CERN
description 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.
id cern-2845830
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28458302023-01-11T21:31:23Zdoi:10.18429/JACoW-IPAC2022-WEPOST013http://cds.cern.ch/record/2845830engVelotti, FrancescoDi Castro, MarioEsposito, Luigi SalvatoreFraser, MatthewGilardoni, SimoneGoddard, BrennanKain, VerenaMatheson, EloiseExploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement LearningAccelerators and Storage RingsThe 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.oai:cds.cern.ch:28458302022
spellingShingle Accelerators and Storage Rings
Velotti, Francesco
Di Castro, Mario
Esposito, Luigi Salvatore
Fraser, Matthew
Gilardoni, Simone
Goddard, Brennan
Kain, Verena
Matheson, Eloise
Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title_full Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title_fullStr Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title_full_unstemmed Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title_short Exploitation of Crystal Shadowing via Multi-Crystal Array, Optimisers and Reinforcement Learning
title_sort exploitation of crystal shadowing via multi-crystal array, optimisers and reinforcement learning
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-IPAC2022-WEPOST013
http://cds.cern.ch/record/2845830
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