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@ARTICLE{Hayrapetyan:628859,
      author       = {Hayrapetyan, Aram and others},
      collaboration = {{CMS Collaboration}},
      title        = {{R}eweighting simulated events using machine-learning
                      techniques in the {CMS} experiment},
      journal      = {The European physical journal / C},
      volume       = {85},
      issn         = {1434-6044},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {PUBDB-2025-01710, arXiv:2411.03023. CMS-MLG-24-001.
                      CERN-EP-2024-269},
      pages        = {495},
      year         = {2025},
      abstract     = {Data analyses in particle physics rely on an accurate
                      simulation of particle collisions and a detailed simulation
                      of detector effects to extract physics knowledge from the
                      recorded data. Event generators together with a GEANT-based
                      simulation of the detectors are used to produce large
                      samples of simulated events for analysis by the LHC
                      experiments. These simulations come at a high computational
                      cost, where the detector simulation and reconstruction
                      algorithms have the largest CPU demands. This article
                      describes how machine-learning (ML) techniques are used to
                      reweight simulated samples obtained with a given set of
                      model parameters to samples with different parameters or
                      samples obtained from entirely different models. The ML
                      reweighting method avoids the need for simulating the
                      detector response multiple times by incorporating the
                      relevant information in a single sample through event
                      weights. Results are presented for reweighting to model
                      variations and higher-order calculations in simulated top
                      quark pair production at the LHC. This ML-based reweighting
                      is an important element of the future computing model of the
                      CMS experiment and will facilitate precision measurements at
                      the High-Luminosity LHC.},
      cin          = {CMS},
      ddc          = {530},
      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
                      $(2019_IVF-HIDSS-0002)$},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)390833306 /
                      $G:(DE-HGF)2019_IVF-HIDSS-0002$},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2411.03023},
      howpublished = {arXiv:2411.03023},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2411.03023;\%\%$},
      pubmed       = {pmid:40342237},
      doi          = {10.1140/epjc/s10052-025-14097-x},
      url          = {https://bib-pubdb1.desy.de/record/628859},
}