Optics corrections using machine learning in the LHC

Optics corrections in the LHC are based on a response matrix between available correctors and observables. Supervised learning has been applied to optics correction in the LHC demonstrating promising results on simulations and demonstrating the ability to reach acceptably low $\beta$-beating. A comp...

Descripción completa

Detalles Bibliográficos
Autores principales: Fol, Elena, Coello de Portugal, Jaime Maria, Franchetti, Giuliano, Tomás, Rogelio
Lenguaje:eng
Publicado: 2019
Materias:
Accelerators and Storage Rings
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2019-THPRB077
http://cds.cern.ch/record/2690544
_version_ 1780963828301824000
author Fol, Elena
Coello de Portugal, Jaime Maria
Franchetti, Giuliano
Tomás, Rogelio
author_facet Fol, Elena
Coello de Portugal, Jaime Maria
Franchetti, Giuliano
Tomás, Rogelio
author_sort Fol, Elena
collection CERN
description Optics corrections in the LHC are based on a response matrix between available correctors and observables. Supervised learning has been applied to optics correction in the LHC demonstrating promising results on simulations and demonstrating the ability to reach acceptably low $\beta$-beating. A comparison of different algorithms to the traditional response matrix approach is given, and it is followed by the presentation of further possible concepts to obtain optics corrections using machine learning (ML).
id oai-inspirehep.net-1745816
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
record_format invenio
spelling oai-inspirehep.net-17458162022-04-08T08:16:27Zdoi:10.18429/JACoW-IPAC2019-THPRB077http://cds.cern.ch/record/2690544engFol, ElenaCoello de Portugal, Jaime MariaFranchetti, GiulianoTomás, RogelioOptics corrections using machine learning in the LHCAccelerators and Storage RingsOptics corrections in the LHC are based on a response matrix between available correctors and observables. Supervised learning has been applied to optics correction in the LHC demonstrating promising results on simulations and demonstrating the ability to reach acceptably low $\beta$-beating. A comparison of different algorithms to the traditional response matrix approach is given, and it is followed by the presentation of further possible concepts to obtain optics corrections using machine learning (ML).CERN-ACC-2019-264oai:inspirehep.net:17458162019
spellingShingle Accelerators and Storage Rings
Fol, Elena
Coello de Portugal, Jaime Maria
Franchetti, Giuliano
Tomás, Rogelio
Optics corrections using machine learning in the LHC
title Optics corrections using machine learning in the LHC
title_full Optics corrections using machine learning in the LHC
title_fullStr Optics corrections using machine learning in the LHC
title_full_unstemmed Optics corrections using machine learning in the LHC
title_short Optics corrections using machine learning in the LHC
title_sort optics corrections using machine learning in the lhc
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-IPAC2019-THPRB077
http://cds.cern.ch/record/2690544
work_keys_str_mv AT folelena opticscorrectionsusingmachinelearninginthelhc
AT coellodeportugaljaimemaria opticscorrectionsusingmachinelearninginthelhc
AT franchettigiuliano opticscorrectionsusingmachinelearninginthelhc
AT tomasrogelio opticscorrectionsusingmachinelearninginthelhc

Ejemplares similares