Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4854
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAad, G.-
dc.contributor.authorAbbott, B.-
dc.contributor.authorAbdallah, J.-
dc.contributor.authorKhalek, S. Abdel-
dc.contributor.authorAbdinov, O.-
dc.contributor.authorAben, R.-
dc.contributor.authorThe ATLAS Collaboration-
dc.contributor.authorSultansoy, Saleh-
dc.date.accessioned2021-09-11T14:20:36Z-
dc.date.available2021-09-11T14:20:36Z-
dc.date.issued2014-
dc.identifier.issn1748-0221-
dc.identifier.urihttps://doi.org/10.1088/1748-0221/9/09/P09009-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/4854-
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.en_US
dc.language.isoenen_US
dc.publisherIop Publishing Ltden_US
dc.relation.ispartofJournal of Instrumentationen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParticle tracking detectorsen_US
dc.subjectParticle tracking detectors (Solid-state detectors)en_US
dc.titleA Neural Network Clustering Algorithm for the Atlas Silicon Pixel Detectoren_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Material Science and Nanotechnology Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümüen_US
dc.identifier.volume9en_US
dc.authorid0000-0002-8644-2349-
dc.authorid0000-0003-2517-531X-
dc.authorid0000-0003-1625-7452-
dc.authorid0000-0002-3222-2738-
dc.authorid0000-0002-9016-138X-
dc.authorid0000-0002-9634-0581-
dc.identifier.wosWOS:000343281300046-
dc.identifier.scopus2-s2.0-84907683450-
dc.institutionauthorSultansoy, Saleh-
dc.identifier.doi10.1088/1748-0221/9/09/P09009-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
dc.identifier.wosqualityQ4-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.grantfulltextnone-
crisitem.author.dept02.6. Department of Material Science and Nanotechnology Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

47
checked on Mar 29, 2025

WEB OF SCIENCETM
Citations

17
checked on Dec 21, 2024

Page view(s)

100
checked on Mar 31, 2025

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.