Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
Lenguaje: | eng |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.18429/JACoW-IPAC2022-WEPOST013 http://cds.cern.ch/record/2845830 |
_version_ | 1780976587066310656 |
---|---|
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 |
work_keys_str_mv | AT velottifrancesco exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT dicastromario exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT espositoluigisalvatore exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT frasermatthew exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT gilardonisimone exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT goddardbrennan exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT kainverena exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning AT mathesoneloise exploitationofcrystalshadowingviamulticrystalarrayoptimisersandreinforcementlearning |