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@ARTICLE{Aad:588855,
      author       = {Aad, Georges and others},
      collaboration = {{ATLAS Collaboration}},
      title        = {{F}ast $b$-tagging at the high-level trigger of the {ATLAS}
                      experiment in {LHC} {R}un 3},
      reportid     = {PUBDB-2023-04995, arXiv:2306.09738. CERN-EP-2023-111},
      year         = {2023},
      note         = {37 pages in total, author list starting page 21, 5 figures,
                      2 tables, submitted to JINST. All figures including
                      auxiliary figures are available at
                      https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/TRIG-2022-03},
      abstract     = {The ATLAS experiment relies on real-time hadronic jet
                      reconstruction and $b$-tagging to record fully hadronic
                      events containing $b$-jets. These algorithms require track
                      reconstruction, which is computationally expensive and could
                      overwhelm the high-level-trigger farm, even at the reduced
                      event rate that passes the ATLAS first stage hardware-based
                      trigger. In LHC Run 3, ATLAS has mitigated these
                      computational demands by introducing a fast
                      neural-network-based $b$-tagger, which acts as a
                      low-precision filter using input from hadronic jets and
                      tracks. It runs after a hardware trigger and before the
                      remaining high-level-trigger reconstruction. This design
                      relies on the negligible cost of neural-network inference as
                      compared to track reconstruction, and the cost reduction
                      from limiting tracking to specific regions of the detector.
                      In the case of Standard Model $HH \rightarrow
                      b\bar{b}b\bar{b}$, a key signature relying on $b$-jet
                      triggers, the filter lowers the input rate to the remaining
                      high-level trigger by a factor of five at the small cost of
                      reducing the overall signal efficiency by roughly 2\%.},
      keywords     = {p p: scattering (INSPIRE) / p p: colliding beams (INSPIRE)
                      / Higgs particle: pair production (INSPIRE) / Higgs
                      particle: hadronic decay (INSPIRE) / bottom: pair production
                      (INSPIRE) / jet: hadronic (INSPIRE) / trigger: hardware
                      (INSPIRE) / jet: trigger (INSPIRE) / jet: bottom (INSPIRE) /
                      bottom: particle identification (INSPIRE) / track data
                      analysis: jet (INSPIRE) / final state: ((n)jet) (INSPIRE) /
                      ATLAS (INSPIRE) / costs (INSPIRE) / CERN LHC Coll (INSPIRE)
                      / efficiency (INSPIRE) / tracks (INSPIRE) / signature
                      (INSPIRE) / neural network (INSPIRE) / statistical analysis
                      (INSPIRE) / data analysis method (INSPIRE)},
      cin          = {ATLAS},
      cid          = {I:(DE-H253)ATLAS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-H253)LHC-Exp-ATLAS-20150101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2306.09738},
      howpublished = {arXiv:2306.09738},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2306.09738;\%\%$},
      doi          = {10.3204/PUBDB-2023-04995},
      url          = {https://bib-pubdb1.desy.de/record/588855},
}