Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32557

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dc.contributor.authorOnofre, A.por
dc.contributor.authorCastro, Nuno Filipe Silva Fernandespor
dc.contributor.authorATLAS Collaboration-
dc.date.accessioned2015-01-08T15:04:03Z-
dc.date.available2015-01-08T15:04:03Z-
dc.date.issued2014-
dc.identifier.citationAad, 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/p09009por
dc.identifier.issn1748-0221-
dc.identifier.urihttp://hdl.handle.net/1822/32557-
dc.description.abstractA 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.por
dc.description.sponsorshipWe 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.por
dc.language.isoengpor
dc.publisherIOP Publishingpor
dc.rightsopenAccesspor
dc.subjectParticle tracking detectorspor
dc.subjectParticle tracking detectors (Solid-state detectors)por
dc.titleA neural network clustering algorithm for the ATLAS silicon pixel detectorpor
dc.typearticlepor
dc.peerreviewedyespor
dc.relation.publisherversionhttp://iopscience.iop.org/1748-0221/9/09/P09009/por
sdum.publicationstatuspublishedpor
oaire.citationStartPage1por
oaire.citationEndPage33por
oaire.citationIssue9por
oaire.citationTitleJournal of Instrumentationpor
oaire.citationVolume9por
dc.identifier.doi10.1088/1748-0221/9/09/p09009por
dc.subject.fosCiências Naturais::Ciências Físicaspor
dc.subject.wosScience & Technologypor
sdum.journalJournal of Instrumentationpor
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