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Data-driven modeling of beam loss in the LHC

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. The level of particle losse...

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Autores principales: Krymova, Ekaterina, Obozinski, Guillaume, Schenk, Michael, Coyle, Loic, Pieloni, Tatiana
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:https://dx.doi.org/10.3389/fphy.2022.960963
http://cds.cern.ch/record/2826159
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author Krymova, Ekaterina
Obozinski, Guillaume
Schenk, Michael
Coyle, Loic
Pieloni, Tatiana
author_facet Krymova, Ekaterina
Obozinski, Guillaume
Schenk, Michael
Coyle, Loic
Pieloni, Tatiana
author_sort Krymova, Ekaterina
collection CERN
description In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. The level of particle losses typically is optimized manually by changing multiple control parameters, among which are, for example, currents in the focusing and defocusing magnets along the collider. It is generally challenging to model and predict losses based on the control parameters due to various (non-linear) effects in the system, such as electron clouds, resonance effects, etc, and multiple sources of uncertainty. At the same time understanding the influence of control parameters on the losses is extremely important in order to improve the operation and performance, and future design of accelerators. Existing results showed that it is hard to generalize the models, which assume the regression model of losses depending on control parameters, from fills carried out throughout one year to the data of another year. To circumvent this, we propose to use an autoregressive modeling approach, where we take into account not only the observed control parameters but also previous loss values. We use an equivalent Kalman Filter (KF) formulation in order to efficiently estimate models with different lags.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
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spelling cern-28261592023-03-31T10:19:20Zdoi:10.3389/fphy.2022.960963http://cds.cern.ch/record/2826159engKrymova, EkaterinaObozinski, GuillaumeSchenk, MichaelCoyle, LoicPieloni, TatianaData-driven modeling of beam loss in the LHCstat.APMathematical Physics and Mathematicsphysics.acc-phAccelerators and Storage RingsIn the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. The level of particle losses typically is optimized manually by changing multiple control parameters, among which are, for example, currents in the focusing and defocusing magnets along the collider. It is generally challenging to model and predict losses based on the control parameters due to various (non-linear) effects in the system, such as electron clouds, resonance effects, etc, and multiple sources of uncertainty. At the same time understanding the influence of control parameters on the losses is extremely important in order to improve the operation and performance, and future design of accelerators. Existing results showed that it is hard to generalize the models, which assume the regression model of losses depending on control parameters, from fills carried out throughout one year to the data of another year. To circumvent this, we propose to use an autoregressive modeling approach, where we take into account not only the observed control parameters but also previous loss values. We use an equivalent Kalman Filter (KF) formulation in order to efficiently estimate models with different lags.arXiv:2208.08935oai:cds.cern.ch:28261592022-08-18
spellingShingle stat.AP
Mathematical Physics and Mathematics
physics.acc-ph
Accelerators and Storage Rings
Krymova, Ekaterina
Obozinski, Guillaume
Schenk, Michael
Coyle, Loic
Pieloni, Tatiana
Data-driven modeling of beam loss in the LHC
title Data-driven modeling of beam loss in the LHC
title_full Data-driven modeling of beam loss in the LHC
title_fullStr Data-driven modeling of beam loss in the LHC
title_full_unstemmed Data-driven modeling of beam loss in the LHC
title_short Data-driven modeling of beam loss in the LHC
title_sort data-driven modeling of beam loss in the lhc
topic stat.AP
Mathematical Physics and Mathematics
physics.acc-ph
Accelerators and Storage Rings
url https://dx.doi.org/10.3389/fphy.2022.960963
http://cds.cern.ch/record/2826159
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AT obozinskiguillaume datadrivenmodelingofbeamlossinthelhc
AT schenkmichael datadrivenmodelingofbeamlossinthelhc
AT coyleloic datadrivenmodelingofbeamlossinthelhc
AT pielonitatiana datadrivenmodelingofbeamlossinthelhc