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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/109015
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/109015 |
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repository_id_str |
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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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1844613879172169728 |
score |
13.070432 |