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@ARTICLE{Clavijo:905636,
      author       = {Clavijo, J. M. and Glaysher, P. and Jitsev, Jenia and
                      Katzy, J. M.},
      title        = {{A}dversarial domain adaptation to reduce sample bias of a
                      high energy physics event classifier},
      journal      = {Machine learning: science and technology},
      volume       = {3},
      number       = {1},
      issn         = {2632-2153},
      address      = {Bristol},
      publisher    = {IOP Publishing},
      reportid     = {FZJ-2022-00863},
      pages        = {015014},
      year         = {2022},
      abstract     = {We apply adversarial domain adaptation in unsupervised
                      setting to reduce sample bias in a supervised high energy
                      physics events classifier training. We make use of a neural
                      network containing event and domain classifier with a
                      gradient reversal layer to simultaneously enable signal
                      versus background events classification on the one hand,
                      while on the other hand minimizing the difference in
                      response of the network to background samples originating
                      from different Monte Carlo models via adversarial domain
                      classification loss. We show the successful bias removal on
                      the example of simulated events at the Large Hadron Collider
                      with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background
                      classification and discuss implications and limitations of
                      the method.},
      cin          = {JSC},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / ZT-I-PF-5-3 - Deep
                      Generative models for fast and precise physics Simulation
                      (DeGeSim) $(2020_ZT-I-PF-5-3)$},
      pid          = {G:(DE-HGF)POF4-5112 / $G:(DE-HGF)2020_ZT-I-PF-5-3$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000734632600001},
      doi          = {10.1088/2632-2153/ac3dde},
      url          = {https://juser.fz-juelich.de/record/905636},
}