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@ARTICLE{Hayrapetyan:617496,
      author       = {Hayrapetyan, Aram and others},
      collaboration = {{CMS Collaboration}},
      title        = {{P}erformance of the {CMS} high-level trigger during {LHC}
                      {R}un 2},
      reportid     = {PUBDB-2024-06827, arXiv:2410.17038. CMS-TRG-19-001.
                      CERN-EP-2024-259},
      year         = {2024},
      note         = {Submitted to the Journal of Instrumentation. All figures
                      and tables can be found at
                      http://cms-results.web.cern.ch/cms-results/public-results/publications/TRG-19-001
                      (CMS Public Pages)},
      abstract     = {The CERN LHC provided proton and heavy ion collisions
                      during its Run 2 operation period from 2015 to 2018.
                      Proton-proton collisions reached a peak instantaneous
                      luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$,
                      twice the initial design value, at $\sqrt{s}$ = 13 TeV. The
                      CMS experiment records a subset of the collisions for
                      further processing as part of its online selection of data
                      for physics analyses, using a two-level trigger system: the
                      Level-1 trigger, implemented in custom-designed electronics,
                      and the high-level trigger, a streamlined version of the
                      offline reconstruction software running on a large computer
                      farm. This paper presents the performance of the CMS
                      high-level trigger system during LHC Run 2 for physics
                      objects, such as leptons, jets, and missing transverse
                      momentum, which meet the broad needs of the CMS physics
                      program and the challenge of the evolving LHC and detector
                      conditions. Sophisticated algorithms that were originally
                      used in offline reconstruction were deployed online.
                      Highlights include a machine-learning b tagging algorithm
                      and a reconstruction algorithm for tau leptons that decay
                      hadronically.},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / DFG
                      project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe
                      (390833306) / HIDSS-0002 - DASHH: Data Science in Hamburg -
                      Helmholtz Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$ / GRK 2497 - GRK 2497: Physik der
                      schwersten Teilchen am Large Hadron Collider (400140256)},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)390833306 /
                      $G:(DE-HGF)2019_IVF-HIDSS-0002$ / G:(GEPRIS)400140256},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2410.17038},
      howpublished = {arXiv:2410.17038},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2410.17038;\%\%$},
      doi          = {10.3204/PUBDB-2024-06827},
      url          = {https://bib-pubdb1.desy.de/record/617496},
}