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@INPROCEEDINGS{Komm:481351,
      author       = {Komm, Matthias},
      title        = {{N}eural network basedprimary vertexreconstruction},
      reportid     = {PUBDB-2022-04324},
      year         = {2022},
      abstract     = {The CMS experiment will be upgraded to maintain physics
                      sensitivity and exploit the higher luminosity of the High
                      Luminosity LHC. Part of this upgrade will see the first
                      level (Level-1) trigger use charged particle tracks within
                      the full outer silicon tracker volume as an input for the
                      first time and new algorithms are being designed to make use
                      of these tracks. One such algorithm is primary vertex
                      finding which is used to identify the hard scatter in an
                      event and separate the primary interaction from additional
                      simultaneous interactions. This work presents a novel
                      approach to regress the primary vertex position and to
                      reject tracks from additional soft interactions, which uses
                      an end-to-end neural network. This neural network possesses
                      simultaneous knowledge of all stages in the reconstruction
                      chain, which allows for end-to-end optimisation. The
                      improved performance of this network versus a baseline
                      approach in the primary vertex regression and
                      track-to-vertex classification is shown. A quantised and
                      pruned version of the neural network is deployed on an FPGA
                      to match the stringent timing and computing requirements of
                      the Level-1 Trigger.},
      month         = {May},
      date          = {2022-05-09},
      organization  = {5th Inter-experiment Machine Learning
                       Workshop, CERN, Geneva (Switzerland), 9
                       May 2022 - 13 May 2022},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611)},
      pid          = {G:(DE-HGF)POF4-611},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://bib-pubdb1.desy.de/record/481351},
}