Performance of the local reconstruction algorithms for the CMS hadron calorimeter with Run 2 data

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dc.authoridGrzanka, Leszek/0000-0002-3599-854X
dc.authoridTytgat, Michael G./0000-0002-3990-2074
dc.authoridMissiroli, Marino/0000-0002-1780-1344
dc.authoridHernández Calama, José María/0000-0001-6436-7547
dc.authoridSingh, Jaikaran/0000-0002-9273-6765
dc.authoridSoares, Mara Senghi/0000-0001-9676-6059
dc.authoridSingh, J B/0000-0001-9029-2462
dc.authorwosidlin, lin/KFB-9548-2024
dc.authorwosidGrzanka, Leszek/M-9052-2018
dc.authorwosidzhang, yan/KHC-3163-2024
dc.authorwosidTytgat, Michael G./F-3732-2018
dc.authorwosidMissiroli, Marino/AAA-9072-2021
dc.authorwosidliu, qi/KHC-7509-2024
dc.authorwosidHernández Calama, José María/AAW-6394-2021
dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAndrejkovic, J. W.
dc.contributor.authorBergauer, T.
dc.contributor.authorChatterjee, S.
dc.contributor.authorDamanakis, K.
dc.contributor.authorDragicevic, M.
dc.date.accessioned2024-06-13T20:18:14Z
dc.date.available2024-06-13T20:18:14Z
dc.date.issued2023
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractA description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015-2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.
dc.description.sponsorshipMarie-Curie program; European Research Council; Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 884104]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the Excellence of Science - EOS - be.h project [30820817]; Being Municipal ScienceAMP; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hellenic Foundation for Research and Innovation (HFRI) (Greece) [2288]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; NKFIH (Hungary) [K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India; National Research Foundation of Korea (NRF/MSIT) (Korea) [2020R1C1C1005916]; Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center (Poland) [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia (Portugal) [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; MCIN/AEI; ERDF a way of making Europe; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project; National Science, Research and Innovation Fund via the Program Management Unit for Human Resources AMP; Institutional Development, Research and Innovation (Thailand) [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.); Hungarian Academy of Sciences; New National Excellence Program - UNKP
dc.description.sponsorshipIndividuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the Excellence of Science - EOS - be.h project n. 30820817; the Being Municipal Science& Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 Quantum Universe - 390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - UNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the National Research Foundation of Korea (NRF/MSIT) grant No. 2020R1C1C1005916 (Korea); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF a way of making Europe, and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).
dc.identifier.doi10.1088/1748-0221/18/11/P11017
dc.identifier.issn1748-0221
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85182444219
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1088/1748-0221/18/11/P11017
dc.identifier.urihttps://hdl.handle.net/11501/1262
dc.identifier.volume18
dc.identifier.wosWOS:001127857800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIop Publishing Ltd
dc.relation.ispartofJournal of Instrumentation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCalorimeters
dc.subjectData Reduction Methods
dc.titlePerformance of the local reconstruction algorithms for the CMS hadron calorimeter with Run 2 data
dc.typeArticle

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