A new calibration method for charm jet identification validated with proton-proton collision events at √s = 13TeV
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Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb−1 at √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses. © 2022 CERN for the benefit of the CMS collaboration
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Excellence of Science 30820817; National Science Foundation, NSF; U.S. Department of Energy, USDOE; Alfred P. Sloan Foundation, APSF; Welch Foundation: C-1845; Kavli Foundation; Alexander von Humboldt-Stiftung, AvH; Nvidia; National Research Centre, NRC; Qatar National Research Fund, QNRF: FSWW-2020-0008; Ministry of Education and Science; Direktion für Entwicklung und Zusammenarbeit, DEZA; Secretaría de Educación Pública, SEP; Türkiye Atom Enerjisi Kurumu, TAEK; Weston Havens Foundation; Thailand Center of Excellence in Physics; Institute for the Promotion of Teaching Science and Technology, IPST; Kanton Zürich; Science and Technology Facilities Council, STFC; European Research Council, ERC; European Cooperation in Science and Technology, COST: CA16108; Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Council of Scientific and Industrial Research, India, CSIR; Department of Atomic Energy, Government of India, DAE; Science Foundation Ireland, SFI; Helmholtz-Gemeinschaft, HGF; Deutsche Forschungsgemeinschaft, DFG: 390833306, 400140256 - GRK2497; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; National Natural Science Foundation of China, NSFC; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Fundação para a Ciência e a Tecnologia, FCT: CEECIND/01334/2018, CERN/FIS-INS/0032/2019, CERN/FIS-PAR/0025/2019; Russian Foundation for Basic Research, РФФИ; Eesti Teadusagentuur, ETAg: PRG445, PRG780, PRG803; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Academy of Finland, AKA; Bundesministerium für Bildung und Forschung, BMBF; Chinese Academy of Sciences, CAS; Austrian Science Fund, FWF; Fonds De La Recherche Scientifique - FNRS, FNRS; Russian Academy of Sciences, РАН; Ministry of Education, MOE; Belgian Federal Science Policy Office, BELSPO; Chulalongkorn University, CU; Eidgenössische Technische Hochschule Zürich, ETH; Ministerio de Ciencia, Tecnología e Innovación Productiva, MINCyT; Opetus- ja Kulttuuriministeriö; Fonds Wetenschappelijk Onderzoek, FWO; Agentschap voor Innovatie door Wetenschap en Technologie, IWT; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, FRIA; Consejo Nacional de Ciencia y Tecnología, CONACYT; Bulgarian National Science Fund, BNSF; Ministry of Education and Science of the Russian Federation, Minobrnauka; Haridus- ja Teadusministeerium, HM; Ministry of Business, Innovation and Employment, MBIE; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Ministry of Science, ICT and Future Planning, MSIP; Ministry of Science and Technology, MOST; National Research Foundation of Korea, NRF; Joint Institute for Nuclear Research, JINR; Magyar Tudományos Akadémia, MTA; Instituto Nazionale di Fisica Nucleare, INFN; A.G. Leventis Foundation; National Science and Technology Development Agency, สวทช; Paul Scherrer Institut, PSI; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS; Narodowe Centrum Nauki, NCN: 2014/15/B/ST2/03998, 2015/19/B/ST2/02861; Stavros Niarchos Foundation, SNF; Universiti Malaya, UM; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; Hrvatska Zaklada za Znanost, HRZZ; Ministry of Education and Science of the Republic of Kazakhstan; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ; National Academy of Sciences of Ukraine, NASU; Centre National de la Recherche Scientifique, CNRS; Universidad Autónoma de San Luis Potosí, UASLP; Latvijas Zinātnes Padome; Ministry of Education and Science, MES; Benemérita Universidad Autónoma de Puebla, BUAP; Universität Zürich, UZH; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA; Ministarstvo Znanosti, Obrazovanja i Sporta, MZOS; Horizon 2020: 675440, 724704, 752730, 758316, 765710, 824093, 884104; Lietuvos Mokslų Akademija; European Regional Development Fund, ERDF; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, CINVESTAV; Pakistan Atomic Energy Commission, PAEC; Beijing Municipal Science and Technology Commission, BMSTC: Z191100007219010; Institut National de Physique Nucléaire et de Physique des Particules, IN2P3; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA: 123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058; Federal Agency of Atomic Energy of the Russian Federation; Sociedad Española de Reumatología, SER; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF; Spanish National Plan for Scientific and Technical Research and Innovation: IDI-2018-000174; Research and Innovation Foundation, RIF; Board of the Swiss Federal Institutes of Technology; Laboratorio Nacional de Supercómputo del Sureste de Mexico, LNS Grant
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keywords
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Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods Calibration; Iterative methods; Machine learning; Calibration method; Charm+; Detector systems; Fitting method; Heavy flavours; Large detector-system performance; Large detectors; Pattern recognition, cluster finding, calibration and fitting method; Proton proton collisions; Systems performance; Pattern recognition
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