Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks

Artificial neural networks can be used for creating surrogate models that can replace computationally expensive simulations. In this paper, a surrogate model was created for a subset of the Compact Linear Collider (CLIC) final-focus system. By training on simulation data, we created a model that map...

Descripción completa

Detalles Bibliográficos
Autores principales: Ögren, J., Gohil, C., Schulte, D.
Lenguaje:eng
Publicado: 2020
Materias:
physics.acc-ph
Accelerators and Storage Rings
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/16/05/P05012
http://cds.cern.ch/record/2741364
_version_ 1780968386391441408
author Ögren, J.
Gohil, C.
Schulte, D.
author_facet Ögren, J.
Gohil, C.
Schulte, D.
author_sort Ögren, J.
collection CERN
description Artificial neural networks can be used for creating surrogate models that can replace computationally expensive simulations. In this paper, a surrogate model was created for a subset of the Compact Linear Collider (CLIC) final-focus system. By training on simulation data, we created a model that maps sextupole offsets to luminosity and beam sizes, thus replacing computationally intensive tracking and beam-beam simulations. This model was then used for optimizing the parameters of a random walk procedure for sextupole alignment.
id cern-2741364
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
record_format invenio
spelling cern-27413642021-07-15T00:44:57Zdoi:10.1088/1748-0221/16/05/P05012http://cds.cern.ch/record/2741364engÖgren, J.Gohil, C.Schulte, D.Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networksphysics.acc-phAccelerators and Storage RingsArtificial neural networks can be used for creating surrogate models that can replace computationally expensive simulations. In this paper, a surrogate model was created for a subset of the Compact Linear Collider (CLIC) final-focus system. By training on simulation data, we created a model that maps sextupole offsets to luminosity and beam sizes, thus replacing computationally intensive tracking and beam-beam simulations. This model was then used for optimizing the parameters of a random walk procedure for sextupole alignment.arXiv:2009.06454oai:cds.cern.ch:27413642020-09-14
spellingShingle physics.acc-ph
Accelerators and Storage Rings
Ögren, J.
Gohil, C.
Schulte, D.
Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title_full Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title_fullStr Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title_full_unstemmed Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title_short Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks
title_sort surrogate modeling of the clic final-focus system using artificial neural networks
topic physics.acc-ph
Accelerators and Storage Rings
url https://dx.doi.org/10.1088/1748-0221/16/05/P05012
http://cds.cern.ch/record/2741364
work_keys_str_mv AT ogrenj surrogatemodelingoftheclicfinalfocussystemusingartificialneuralnetworks
AT gohilc surrogatemodelingoftheclicfinalfocussystemusingartificialneuralnetworks
AT schulted surrogatemodelingoftheclicfinalfocussystemusingartificialneuralnetworks

Ejemplares similares