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Centrality determination in heavy-ion collisions with the LHCb detector

  • LHCb Collaboration

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The centrality of heavy-ion collisions is directly related to the created medium in these interactions. A procedure to determine the centrality of collisions with the LHCb detector is implemented for lead-lead collisions at √s NN = 5 TeV and lead-neon fixed-target collisions at √s NN = 69 GeV. The energy deposits in the electromagnetic calorimeter are used to determine and define the centrality classes. The correspondence between the number of participants and the centrality for the lead-lead collisions is in good agreement with the correspondence found in other experiments, and the centrality measurements for the lead-neon collisions presented here are performed for the first time in fixed-target collisions at the LHC.

Original languageEnglish
Article numberP05009
Pages (from-to)1-32
Number of pages32
JournalJournal of Instrumentation
Volume17
Issue number5
Early online date5 May 2022
DOIs
Publication statusPublished - May 2022

Bibliographical note

Funding Information:
We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from CERN and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN (Italy); NWO (Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MSHE (Russia); MICINN (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); DOE NP and NSF (U.S.A.). We acknowledge the computing resources that are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy), SURF (Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex LLC (Russia), CSCS (Switzerland), IFINHH (Romania), CBPF (Brazil), PL-GRID (Poland) and NERSC (U.S.A.). We are indebted to the communities behind the multiple open-source software packages on which we depend. Individual groups or members have received support from ARC and ARDC (Australia); AvH Foundation (Germany); EPLANET, Marie Sk odowska-Curie Actions and ERC (European Union); A∗MIDEX, ANR, IPhU and Labex P2IO, and Région Auvergne-Rhône-Alpes (France); Key Research Program of Frontier Sciences of CAS, CAS PIFI, CAS CCEPP, Fundamental Research Funds for the Central Universities, and Sci. & Tech. Program of Guangzhou (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Leverhulme Trust, the Royal Society and UKRI (United Kingdom).

Publisher Copyright:
© 2022 CERN for the benefit of the LHCb collaboration.

Funding

We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from CERN and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN (Italy); NWO (Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MSHE (Russia); MICINN (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); DOE NP and NSF (U.S.A.). We acknowledge the computing resources that are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy), SURF (Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex LLC (Russia), CSCS (Switzerland), IFINHH (Romania), CBPF (Brazil), PL-GRID (Poland) and NERSC (U.S.A.). We are indebted to the communities behind the multiple open-source software packages on which we depend. Individual groups or members have received support from ARC and ARDC (Australia); AvH Foundation (Germany); EPLANET, Marie Sk odowska-Curie Actions and ERC (European Union); A∗MIDEX, ANR, IPhU and Labex P2IO, and Région Auvergne-Rhône-Alpes (France); Key Research Program of Frontier Sciences of CAS, CAS PIFI, CAS CCEPP, Fundamental Research Funds for the Central Universities, and Sci. & Tech. Program of Guangzhou (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Leverhulme Trust, the Royal Society and UKRI (United Kingdom).

FundersFunder number
Agence Nationale de la Recherche
Australian Research Council
Australian Research Data Commons
National Academy of Sciences of Ukraine
Narodowe Centrum Nauki
National Science Foundation
Institut national de physique nucléaire et de physique des particules
H2020 Marie Skłodowska-Curie Actions
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Ministerio de Ciencia e Innovación
Ministry of Science and Technology, Taiwan
Program of Guangzhou
Leverhulme Trust
PL-GRID
SURF
Instituto Nazionale di Fisica Nucleare
National Energy Research Scientific Computing Center
Financiadora de Estudos e Projetos
Centre National de la Recherche Scientifique
Bundesministerium für Bildung und Forschung
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Karlsruhe Institute of Technology
Deutsche Forschungsgemeinschaft
Région Auvergne-Rhône-Alpes
Russian Foundation for Basic Research
U.S. Department of Energy
Yandex LLC
CAS CCEPP
GridPP
RRCKI
IFINHH
Russian Science Foundation
Deutsches Elektronen-Synchrotron
Chinese Academy of Sciences
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
XuntaGal
Key Research Program of Frontier Sciences of CAS
Royal Society
Sociedad Española de Reumatología
European Research Council
Ministerstwo Edukacji i Nauki
Generalitat Valenciana
CERN
Fundamental Research Funds for the Central Universities
Alexander von Humboldt-Stiftung
UK Research and Innovation
National Natural Science Foundation of China
Science and Technology Facilities CouncilST/S000712/1

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    Keywords

    • Pattern recognition, cluster finding, calibration and fitting methods
    • Performance of High Energy Physics Detectors
    • Simulation methods and programs

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