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@ARTICLE{Hayrapetyan:619203,
      author       = {Hayrapetyan, A. and others},
      collaboration = {{CMS Collaboration}},
      title        = {{P}erformance of the {CMS} high-level trigger during {LHC}
                      {R}un 2},
      journal      = {Journal of Instrumentation},
      volume       = {19},
      number       = {11},
      issn         = {1748-0221},
      address      = {London},
      publisher    = {Inst. of Physics},
      reportid     = {PUBDB-2024-07461, arXiv:2410.17038. CMS-TRG-19-001.
                      CERN-EP-2024-259},
      pages        = {P11021},
      year         = {2024},
      abstract     = {The CERN LHC provided proton and heavy ion collisions
                      duringits Run 2 operation period from 2015 to 2018.
                      Proton-protoncollisions reached a peak instantaneous
                      luminosity of2.1× 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the
                      initialdesign value, at √(s)=13 TeV. The CMS
                      experimentrecords a subset of the collisions for further
                      processing as part ofits online selection of data for
                      physics analyses, using a two-leveltrigger system: the
                      Level-1 trigger, implemented in custom-designedelectronics,
                      and the high-level trigger, a streamlined version ofthe
                      offline reconstruction software running on a large
                      computerfarm. This paper presents the performance of the CMS
                      high-leveltrigger system during LHC Run 2 for physics
                      objects, such asleptons, jets, and missing transverse
                      momentum, which meet the broadneeds of the CMS physics
                      program and the challenge of the evolvingLHC and detector
                      conditions. Sophisticated algorithms that wereoriginally
                      used in offline reconstruction were deployedonline.
                      Highlights include a machine-learning b tagging algorithmand
                      a reconstruction algorithm for tau leptons that
                      decayhadronically.},
      keywords     = {Large detector systems for particle and astroparticle
                      physics (autogen) / Trigger concepts and systems (hardware
                      and software) (autogen)},
      cin          = {CMS},
      ddc          = {610},
      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)16},
      eprint       = {2410.17038},
      howpublished = {arXiv:2410.17038},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2410.17038;\%\%$},
      UT           = {WOS:001389636600001},
      doi          = {10.1088/1748-0221/19/11/P11021},
      url          = {https://bib-pubdb1.desy.de/record/619203},
}