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@ARTICLE{Aad:646428,
      author       = {Aad, Georges and others},
      collaboration = {{ATLAS Collaboration}},
      title        = {{T}ransforming jet flavour tagging at {ATLAS}},
      journal      = {Nature Communications},
      volume       = {17},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {PUBDB-2026-00849, arXiv:2505.19689. CERN-EP-2025-103.
                      arXiv:2505.19689. CERN-EP-2025-103},
      pages        = {541},
      year         = {2026},
      note         = {39 pages in total, author list starting page 22, 6 figures,
                      1 table, submitted to Nature Communications. All figures
                      including auxiliary figures are available at
                      https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/FTAG-2023-05/},
      abstract     = {Jet flavour tagging enables the identification of jets
                      originating from heavy-flavour quarks in proton–proton
                      collisions at the Large Hadron Collider, playing a critical
                      role in its physics programmes. This paper presents GN2, a
                      transformer-based flavour tagging algorithm deployed by the
                      ATLAS Collaboration that represents a different methodology
                      compared to previous approaches. Designed to classify jets
                      based on the flavour of their constituent particles, GN2
                      processes low-level tracking information in an end-to-end
                      architecture and incorporates physics-informed auxiliary
                      training objectives to enhance both interpretability and
                      performance. Its performance is validated in both simulation
                      and collision data. The measured c-jet (light-jet) rejection
                      in data is improved by a factor of 3.5 (1.8) for a $70\%$
                      b-jet tagging efficiency, compared to the previous
                      algorithm. GN2 provides substantial benefits for physics
                      analyses involving heavy-flavour jets, such as measurements
                      of Higgs boson pair production and the couplings of bottom
                      and charm quarks to the Higgs boson, and demonstrates the
                      impact of advanced machine learning methods in experimental
                      particle physics.},
      cin          = {ATLAS},
      ddc          = {500},
      cid          = {I:(DE-H253)ATLAS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / DFG
                      project G:(GEPRIS)469666862 - Präzisionstests des
                      Standardmodells unter der Verwendung von geboosteten
                      W/Z-Bosonen am Large Hadron Collider (469666862) / REALDARK
                      - REAL-time discovery strategies for DARK matter and dark
                      sector signals at the ATLAS detector with Run-3 LHC data
                      (101002463) / DITTO - Comprehensive search for new phenomena
                      in the dilepton spectrum at the LHC (101089007) / BARD -
                      B-resonance Algorithm using Rare Decays (101116429)},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)469666862 /
                      G:(EU-Grant)101002463 / G:(EU-Grant)101089007 /
                      G:(EU-Grant)101116429},
      experiment   = {EXP:(DE-H253)LHC-Exp-ATLAS-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {2505.19689},
      howpublished = {arXiv:2505.19689},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2505.19689;\%\%$},
      doi          = {10.1038/s41467-025-65059-6},
      url          = {https://bib-pubdb1.desy.de/record/646428},
}