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@TECHREPORT{Collaboration:600627,
      key          = {600627},
      collaboration = {{ATLAS Collaboration}},
      title        = {{C}lustering and {T}racking in {D}ense {E}nvironments with
                      the {ATLAS} {I}nner {T}racker for the {H}igh-{L}uminosity
                      {LHC}},
      number       = {ATL-PHYS-PUB-2023-022},
      reportid     = {PUBDB-2023-08050, ATL-PHYS-PUB-2023-022},
      pages        = {29},
      year         = {2023},
      note         = {All figures including auxiliary figures are available at
                      https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2023-022.
                      The authors list may be incomplete!},
      abstract     = {Dense hadronic environments encountered, for example, in
                      the core of high-transverse- momentum jets, present specific
                      challenges for the reconstruction of charged-particle
                      trajectories (tracks) in the ATLAS inner tracking detectors,
                      as they are characterised by a high density of ionising
                      particles. The energy deposits (clusters) left by these
                      particles in the silicon sensors are more likely to merge
                      with increasing particle densities, especially in the
                      innermost layers of the ATLAS silicon-pixel detectors. This
                      has detrimental effects on both the track reconstruction
                      efficiency and the precision with which the track parameters
                      can be measured. The track reconstruction software for the
                      current ATLAS Inner Detector (ID) relies on dedicated
                      machine-learning based algorithms to amend these problems by
                      identifying merged clusters and estimating the positions of
                      the individual sub-clusters. The new Inner Tracker (ITk),
                      which will replace the ID for the High-Luminosity LHC
                      programme, features an improved granularity due to its
                      smaller pixel sensor size, which is expected to reduce
                      cluster merging rates in dense environments. In this note, a
                      comprehensive study of the clustering and tracking
                      performance in dense environments with a recent ITk layout
                      is presented. Different quantities are studied to assess the
                      effects of cluster merging at the cluster-, track-, and
                      jet-level.},
      keywords     = {p p: scattering (INSPIRE) / p p: colliding beams (INSPIRE)
                      / semiconductor detector: pixel (INSPIRE) / jet: transverse
                      momentum (INSPIRE) / transverse momentum: high (INSPIRE) /
                      track data analysis: efficiency (INSPIRE) / cluster: effect
                      (INSPIRE) / particle: density (INSPIRE) / density: high
                      (INSPIRE) / charged particle: trajectory (INSPIRE) / ATLAS
                      (INSPIRE) / tracking detector (INSPIRE) / tracks (INSPIRE) /
                      CERN LHC Coll: upgrade (INSPIRE) / programming (INSPIRE) /
                      machine learning (INSPIRE) / performance (INSPIRE) / data
                      analysis method (INSPIRE) / numerical calculations: Monte
                      Carlo (INSPIRE) / CTIDE (autogen) / TRACKING (autogen)},
      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)29},
      doi          = {10.3204/PUBDB-2023-08050},
      url          = {https://bib-pubdb1.desy.de/record/600627},
}