A neural network clustering algorithm for the ATLAS silicon pixel detector

Autores
Aad, G.; Abbott, B.; Abdallah, J.; Abdel Khalek, S.; Abdinov, O.; Otero y Garzon, Gustavo Javier; Piegaia, Ricardo Nestor; Sacerdoti, Sabrina; Reisin, Hernan Diego; Romeo, Gaston Leonardo; Alconada Verzini, María Josefina; Alonso, Francisco; Anduaga, Xabier Sebastian; Dova, Maria Teresa; Monticelli, Fernando Gabriel; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zobernig, G.; Zoccoli, A.; Nedden, M. zur; Zurzolo, G.; Zutshi, V.; Zwalinski, L.; The ATLAS Collaboration
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
Fil: Alconada Verzini, María Josefina.
Materia
ATLAS
Neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/109015

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network_name_str CONICET Digital (CONICET)
spelling A neural network clustering algorithm for the ATLAS silicon pixel detectorAad, G.Abbott, B.Abdallah, J.Abdel Khalek, S.Abdinov, O.Otero y Garzon, Gustavo JavierPiegaia, Ricardo NestorSacerdoti, SabrinaReisin, Hernan DiegoRomeo, Gaston LeonardoAlconada Verzini, María JosefinaAlonso, FranciscoAnduaga, Xabier SebastianDova, Maria TeresaMonticelli, Fernando GabrielZhukov, K.Zibell, A.Zieminska, D.Zimine, N. I.Zimmermann, C.Zimmermann, R.Zimmermann, S.Zimmermann, S.Ziolkowski, M.Zobernig, G.Zoccoli, A.Nedden, M. zurZurzolo, G.Zutshi, V.Zwalinski, L.The ATLAS CollaborationATLASNeural networkshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.Fil: Alconada Verzini, María Josefina.IOP Publishing2014-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/109015Aad, G.; Abbott, B.; Abdallah, J.; Abdel Khalek, S.; Abdinov, O.; et al.; A neural network clustering algorithm for the ATLAS silicon pixel detector; IOP Publishing; Journal of Instrumentation; 9; 10-2014; 1-351748-0221CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1088/1748-0221/9/09/P09009info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1748-0221/9/09/P09009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:04:54Zoai:ri.conicet.gov.ar:11336/109015instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:04:54.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A neural network clustering algorithm for the ATLAS silicon pixel detector
title A neural network clustering algorithm for the ATLAS silicon pixel detector
spellingShingle A neural network clustering algorithm for the ATLAS silicon pixel detector
Aad, G.
ATLAS
Neural networks
title_short A neural network clustering algorithm for the ATLAS silicon pixel detector
title_full A neural network clustering algorithm for the ATLAS silicon pixel detector
title_fullStr A neural network clustering algorithm for the ATLAS silicon pixel detector
title_full_unstemmed A neural network clustering algorithm for the ATLAS silicon pixel detector
title_sort A neural network clustering algorithm for the ATLAS silicon pixel detector
dc.creator.none.fl_str_mv Aad, G.
Abbott, B.
Abdallah, J.
Abdel Khalek, S.
Abdinov, O.
Otero y Garzon, Gustavo Javier
Piegaia, Ricardo Nestor
Sacerdoti, Sabrina
Reisin, Hernan Diego
Romeo, Gaston Leonardo
Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastian
Dova, Maria Teresa
Monticelli, Fernando Gabriel
Zhukov, K.
Zibell, A.
Zieminska, D.
Zimine, N. I.
Zimmermann, C.
Zimmermann, R.
Zimmermann, S.
Zimmermann, S.
Ziolkowski, M.
Zobernig, G.
Zoccoli, A.
Nedden, M. zur
Zurzolo, G.
Zutshi, V.
Zwalinski, L.
The ATLAS Collaboration
author Aad, G.
author_facet Aad, G.
Abbott, B.
Abdallah, J.
Abdel Khalek, S.
Abdinov, O.
Otero y Garzon, Gustavo Javier
Piegaia, Ricardo Nestor
Sacerdoti, Sabrina
Reisin, Hernan Diego
Romeo, Gaston Leonardo
Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastian
Dova, Maria Teresa
Monticelli, Fernando Gabriel
Zhukov, K.
Zibell, A.
Zieminska, D.
Zimine, N. I.
Zimmermann, C.
Zimmermann, R.
Zimmermann, S.
Ziolkowski, M.
Zobernig, G.
Zoccoli, A.
Nedden, M. zur
Zurzolo, G.
Zutshi, V.
Zwalinski, L.
The ATLAS Collaboration
author_role author
author2 Abbott, B.
Abdallah, J.
Abdel Khalek, S.
Abdinov, O.
Otero y Garzon, Gustavo Javier
Piegaia, Ricardo Nestor
Sacerdoti, Sabrina
Reisin, Hernan Diego
Romeo, Gaston Leonardo
Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastian
Dova, Maria Teresa
Monticelli, Fernando Gabriel
Zhukov, K.
Zibell, A.
Zieminska, D.
Zimine, N. I.
Zimmermann, C.
Zimmermann, R.
Zimmermann, S.
Ziolkowski, M.
Zobernig, G.
Zoccoli, A.
Nedden, M. zur
Zurzolo, G.
Zutshi, V.
Zwalinski, L.
The ATLAS Collaboration
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ATLAS
Neural networks
topic ATLAS
Neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
Fil: Alconada Verzini, María Josefina.
description A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/109015
Aad, G.; Abbott, B.; Abdallah, J.; Abdel Khalek, S.; Abdinov, O.; et al.; A neural network clustering algorithm for the ATLAS silicon pixel detector; IOP Publishing; Journal of Instrumentation; 9; 10-2014; 1-35
1748-0221
CONICET Digital
CONICET
url http://hdl.handle.net/11336/109015
identifier_str_mv Aad, G.; Abbott, B.; Abdallah, J.; Abdel Khalek, S.; Abdinov, O.; et al.; A neural network clustering algorithm for the ATLAS silicon pixel detector; IOP Publishing; Journal of Instrumentation; 9; 10-2014; 1-35
1748-0221
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1088/1748-0221/9/09/P09009
info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1748-0221/9/09/P09009
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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