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Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at (Formula presented)

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Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to (Formula presented) or (Formula presented), have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at (Formula presented) during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed. © 2025 Institute of Physics. All rights reserved.

Anahtar Kelimeler

calibration and fitting methodscluster findingPattern recognitionPerformance of High Energy Physics DetectorsBosonsCalibrationJetsLearning systemsParticle detectorsTopologyCalibration methodFitting methodHeavy flavoursHiggs bosonHigh energy physics detectorLorentzPerformancePerformance of high energy physic detectorProton proton collisions
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AlanDeğerDil
dc.contributor.authorHayrapetyan, A.
dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAndrejkovic, J.W.
dc.contributor.authorBenato, L.
dc.contributor.authorBergauer, T.
dc.contributor.authorChatterjee, S.
dc.contributor.authorDamanakis, K.
dc.contributor.authorDragicevic, M.
dc.contributor.authorHussain, P.S.
dc.contributor.authorJeitler, M.
dc.contributor.authorKrammer, N.
dc.contributor.authorLi, A.
dc.contributor.authorLiko, D.
dc.contributor.authorMikulec, I.
dc.contributor.authorSchieck, J.
dc.contributor.authorSchöfbeck, R.
dc.contributor.authorSchwarz, D.
dc.contributor.authorSonawane, M.
dc.contributor.authorWaltenberger, W.
dc.contributor.authorWulz, C.-E.
dc.contributor.authorJanssen, T.
dc.contributor.authorVan Laer, T.
dc.contributor.authorVan Mechelen, P.
dc.contributor.authorBreugelmans, N.
dc.contributor.authorD’Hondt, J.
dc.contributor.authorDansana, S.
dc.contributor.authorDe Moor, A.
dc.contributor.authorDelcourt, M.
dc.contributor.authorHeyen, F.
dc.contributor.authorLowette, S.
dc.contributor.authorMakarenko, I.
dc.contributor.authorMüller, D.
dc.contributor.authorTavernier, S.
dc.contributor.authorTytgat, M.
dc.contributor.authorVan Onsem, G.P.
dc.contributor.authorVan Putte, S.
dc.contributor.authorVannerom, D.
dc.contributor.authorBilin, B.
dc.contributor.authorClerbaux, B.
dc.contributor.authorDas, A.K.
dc.contributor.authorDe Bruyn, I.
dc.contributor.authorDe Lentdecker, G.
dc.contributor.authorEvard, H.
dc.contributor.authorFavart, L.
dc.contributor.authorGianneios, P.
dc.contributor.authorJaramillo, J.
dc.contributor.authorKhalilzadeh, A.
dc.contributor.authorKhan, F.A.
dc.contributor.authorLee, K.
dc.date.accessioned2026-10-18T20:25:55.577279364Z
dc.date.available2026-10-18T20:25:55.577279364Z
dc.date.issued2025
dc.description.abstractMeasurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to (Formula presented) or (Formula presented), have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at (Formula presented) during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed. © 2025 Institute of Physics. All rights reserved.
dc.description.sponsorshipWe congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, TARISTU24-TK10 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMFTR, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIT and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES, NSC, and NAWA (Poland); FCT (Portugal); MESTD (Serbia); MICIU/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI (Thailand); TUBITAK and TENMAK (T\u00FCrkiye); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.). Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, 101001205, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Fonds pour la Formation \u00E0 la Recherche dans l\u2019Industrie et dans l\u2019Agriculture (FRIA) and Fonds voor Wetenschappelijk Onderzoek contract No. 1228724N (Belgium); the Beijing Municipal Science & Technology Commission, No. Z191100007219010, the Fundamental Research Funds for the Central Universities, the Ministry of Science and Technology of China under Grant No. 2023YFA1605804, the Natural Science Foundation of China under Grant No. 12061141002, 12535004, and USTC Research Funds of the Double First-Class Initiative No. YD2030002017 (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Agence Nationale de la Recherche grant ANR-22-CE31-0002 and the Interdisciplinary Thematic Institute in Quantum Science and Nanomaterials (QMat) (France); the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany\u2019s Excellence Strategy \u2014 EXC 2121 \u201CQuantum Universe\u201D \u2014 390833306, and under project number 400140256 \u2014 GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program \u2014 \u00DANKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64, and 2021-4.1.2-NEMZ_KI-2024-00036 (Hungary); the Council of Science and Industrial Research, India; ICSC \u2014 National Research Centre for High Performance Computing, Big Data and Quantum Computing, FAIR \u2014 Future Artificial Intelligence Research, and CUP I53D23001070006 (Mission 4 Component 1), funded by the NextGenerationEU program (Italy); 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, 2021/43/B/ST2/01552, 2023/49/B/ST2/03273, and the NAWA contract BPN/PPO/2021/1/00011 (Poland); the Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MICIU/AEI/10.13039/501100011033, ERDF/EU, \u201CEuropean Union NextGenerationEU/PRTR\u201D, and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, the National Science, Research and Innovation Fund program IND_FF_68_369_2300_097, and the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B39G680009 (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.doi10.1088/1748-0221/20/11/p11006
dc.identifier.issn1748-0221
dc.identifier.issue11
dc.identifier.scopus2-s2.0-105035824091
dc.identifier.startpageP11006
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/10788
dc.identifier.volume20
dc.language.isoen
dc.publisherInstitute of Physics
dc.relation.ispartofJournal of Instrumentation
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectcalibration and fitting methods
dc.subjectcluster finding
dc.subjectPattern recognition
dc.subjectPerformance of High Energy Physics Detectors
dc.subjectBosons
dc.subjectCalibration
dc.subjectJets
dc.subjectLearning systems
dc.subjectParticle detectors
dc.subjectTopology
dc.subjectCalibration method
dc.subjectFitting method
dc.subjectHeavy flavours
dc.subjectHiggs boson
dc.subjectHigh energy physics detector
dc.subjectLorentz
dc.subjectPerformance
dc.subjectPerformance of high energy physic detector
dc.subjectProton proton collisions
dc.titlePerformance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at (Formula presented)
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus6
rdl.oa.statusgold
rdl.source.indexSCOPUS