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@ARTICLE{Sirunyan:450152,
      author       = {Sirunyan, Albert M and others},
      title        = {{A} deep neural network for simultaneous estimation of b
                      jet energy and resolution},
      journal      = {Computing and software for big science},
      volume       = {4},
      number       = {1},
      issn         = {2510-2044},
      address      = {Cham, Switzerland},
      publisher    = {Springer International Publishing},
      reportid     = {PUBDB-2020-04208, arXiv:1912.06046. CMS-HIG-18-027.
                      CERN-EP-2019-261. arXiv:1912.06046. CMS-HIG-18-027.
                      CERN-EP-2019-261},
      pages        = {10},
      year         = {2020},
      note         = {All figures and tables can be found at
                      http://cms-results.web.cern.ch/cms-results/public-results/publications/HIG-18-027
                      (CMS Public Pages)},
      abstract     = {We describe a method to obtain point and dispersion
                      estimates for the energies of jets arising from b quarks
                      produced in proton–proton collisions at an energy of
                      $\sqrt{s}=13\,\text {TeV} $ at the CERN LHC. The algorithm
                      is trained on a large sample of simulated b jets and
                      validated on data recorded by the CMS detector in 2017
                      corresponding to an integrated luminosity of 41 $\,\text
                      {fb}^{-1}$. A multivariate regression algorithm based on a
                      deep feed-forward neural network employs jet composition and
                      shape information, and the properties of reconstructed
                      secondary vertices associated with the jet. The results of
                      the algorithm are used to improve the sensitivity of
                      analyses that make use of b jets in the final state, such as
                      the observation of Higgs boson decay to $\hbox {b}\bar{\hbox
                      {b}}$.},
      keywords     = {jet: bottom (autogen) / jet: energy (autogen) / Higgs
                      particle: decay (autogen) / vertex: secondary (autogen) / p
                      p: scattering (autogen) / neural network (autogen) / CERN
                      LHC Coll (autogen) / sensitivity (autogen) / dispersion
                      (autogen) / resolution (autogen) / CERN Lab (autogen) / CMS
                      (autogen)},
      cin          = {CMS},
      ddc          = {004},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF3-611)},
      pid          = {G:(DE-HGF)POF3-611},
      experiment   = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
      typ          = {PUB:(DE-HGF)16},
      eprint       = {1912.06046},
      howpublished = {arXiv:1912.06046},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:1912.06046;\%\%$},
      pubmed       = {pmid:33196702},
      doi          = {10.1007/s41781-020-00041-z},
      url          = {https://bib-pubdb1.desy.de/record/450152},
}