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@TECHREPORT{Collaboration:600611,
      key          = {600611},
      collaboration = {{ATLAS Collaboration}},
      title        = {{E}lectron {I}dentification with a {C}onvolutional {N}eural
                      {N}etwork in the {ATLAS} {E}xperiment},
      number       = {ATL-PHYS-PUB-2023-001},
      reportid     = {PUBDB-2023-08035, ATL-PHYS-PUB-2023-001},
      pages        = {33},
      year         = {2023},
      note         = {All figures including auxiliary figures are available at
                      https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2023-001.
                      The authors list may be incomplete!},
      abstract     = {The identification of electrons plays an important role for
                      a large fraction of the physics analyses performed at the
                      ATLAS experiment. An improved electron identification
                      algorithm is presented that is based on convolutional neural
                      networks (CNN), a type of machine learning architecture
                      specialized in image recognition. It takes as input the
                      images of the deposited energy in the calorimeter cells
                      around the reconstructed electron candidates for each of the
                      electromagnetic and hadronic calorimeter layers. Additional
                      input features include the same high-level variables that
                      are used by the likelihood (LLH) and deep neural network
                      (DNN) algorithms developed in ATLAS, as well as the
                      information of up to five inner detector tracks that are
                      matched to an electron candidate during its reconstruction.
                      The output of the network corresponds to the probability
                      that a reconstructed electron belongs to six classes of
                      signals and backgrounds. A significant improvement in
                      identification performance is observed when the CNN
                      algorithm is used in the simulation. For example, for a
                      working point that corresponds to the same signal efficiency
                      as the LLH 'Loose' working point, the CNN improves the
                      rejection against charged hadrons faking signal electrons,
                      the dominant electron background at the LHC, by factors of 5
                      to 8 (depending on the electron kinematics) with respect to
                      the LLH. For the most difficult background constituted of
                      electrons originating from heavy flavour hadron decays, the
                      background rejection of the CNN is improved by factors
                      varying between about 2 to 3.5 with respect to the LLH.},
      keywords     = {p p: scattering (INSPIRE) / p p: colliding beams (INSPIRE)
                      / electron: background (INSPIRE) / electron: particle
                      identification (INSPIRE) / hadron: decay (INSPIRE) /
                      calorimeter: hadronic (INSPIRE) / ATLAS (INSPIRE) / neural
                      network (INSPIRE) / network (INSPIRE) / calorimeter:
                      electromagnetic (INSPIRE) / tracks (INSPIRE) / machine
                      learning (INSPIRE) / particle identification: efficiency
                      (INSPIRE) / CERN LHC Coll (INSPIRE) / kinematics (INSPIRE) /
                      particle identification: performance (INSPIRE) / heavy
                      quark: decay (INSPIRE) / statistical analysis (INSPIRE) /
                      data analysis method (INSPIRE) / numerical calculations:
                      Monte Carlo (INSPIRE) / experimental results (INSPIRE) /
                      EGAMMA (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-2023-08035},
      url          = {https://bib-pubdb1.desy.de/record/600611},
}