A neural network clustering algorithm for the ATLAS silicon pixel detector
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/32557 |
Resumo: | 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. |
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A neural network clustering algorithm for the ATLAS silicon pixel detectorParticle tracking detectorsParticle tracking detectors (Solid-state detectors)Ciências Naturais::Ciências FísicasScience & TechnologyA 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.We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.IOP PublishingUniversidade do MinhoOnofre, A.Castro, Nuno Filipe Silva FernandesATLAS Collaboration20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/32557engAad, G., Abbott, B., Abdallah, J., Khalek, S. A., Abdinov, O., Aben, R., . . . Collaboration, A. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. Journal of Instrumentation, 9. doi: 10.1088/1748-0221/9/09/p090091748-022110.1088/1748-0221/9/09/p09009http://iopscience.iop.org/1748-0221/9/09/P09009/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:51:11Zoai:repositorium.sdum.uminho.pt:1822/32557Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:50:01.826201Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
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 Onofre, A. Particle tracking detectors Particle tracking detectors (Solid-state detectors) Ciências Naturais::Ciências Físicas Science & Technology |
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 |
author |
Onofre, A. |
author_facet |
Onofre, A. Castro, Nuno Filipe Silva Fernandes ATLAS Collaboration |
author_role |
author |
author2 |
Castro, Nuno Filipe Silva Fernandes ATLAS Collaboration |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Onofre, A. Castro, Nuno Filipe Silva Fernandes ATLAS Collaboration |
dc.subject.por.fl_str_mv |
Particle tracking detectors Particle tracking detectors (Solid-state detectors) Ciências Naturais::Ciências Físicas Science & Technology |
topic |
Particle tracking detectors Particle tracking detectors (Solid-state detectors) Ciências Naturais::Ciências Físicas Science & Technology |
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 2014-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/32557 |
url |
http://hdl.handle.net/1822/32557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Aad, G., Abbott, B., Abdallah, J., Khalek, S. A., Abdinov, O., Aben, R., . . . Collaboration, A. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. Journal of Instrumentation, 9. doi: 10.1088/1748-0221/9/09/p09009 1748-0221 10.1088/1748-0221/9/09/p09009 http://iopscience.iop.org/1748-0221/9/09/P09009/ |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IOP Publishing |
publisher.none.fl_str_mv |
IOP Publishing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133084175564800 |