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dc.creatorĐorđević, Miloš
dc.date.accessioned2018-12-04T13:58:09Z
dc.date.available2018-12-04T13:58:09Z
dc.date.issued2018
dc.identifier.issn2101-6275 (print)
dc.identifier.urihttps://www.epj-conferences.org/10.1051/epjconf/201819102016
dc.identifier.urihttp://vinar.vin.bg.ac.rs/handle/123456789/7963
dc.description.abstractThe event reconstruction at the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider (LHC) is predominantly based on the Particle Flow algorithm. This algorithm for a global event description uses the information from all subdetector systems, unlike the previous, traditional approaches that were focused on the localized information in each subdetector. These traditional methods use the raw information (tracks, hits), while the Particle Flow algorithm completely reconstructs the event by identifying and reconstructing the comprehensive list of final-state particles (photons, electrons, muons, charged and neutral hadrons), resulting in superior reconstruction of jets, missing transverse energy, tau leptons, electrons and muons. This approach also allows for efficient identification and mitigation of the pileup effect. The concept and performance of the Particle Flow algorithm, together with the prospects for its development in the context of the upgraded CMS detector, are presented in this overview. © The Authors, published by EDP Sciences, 2018.
dc.publisherEPJ Web of Conferences
dc.rightsopenAccess
dc.source20th International Seminar on High Energy Physics - QUARKS 2018; Valday, Russian Federation, May 27th-June 2nd 2018
dc.titleThe CMS Particle Flow Algorithmen
dc.typeconferenceObjecten
dc.rights.licenseBY
dcterms.abstractЗхезхер, Y.В.; Ђорђевић, Милош; Волкова, В.Е.; Матвеев, В.A.; Левков, Д.Г.; Рубаков, В.A.;
dc.rights.holderthe Authors
dc.citation.volume191
dc.citation.spage02016
dc.identifier.doi10.1051/epjconf/201819102016
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85056737833
dc.identifier.fulltexthttp://vinar.vin.bg.ac.rs//bitstream/id/10823/epjconf_quarks2018_02016.pdf


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