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@INPROCEEDINGS{Bach:605866,
      author       = {Bach, Jörn and Schwanenberger, Christian and Stelldinger,
                      Peer and Grohsjean, Alexander},
      title        = {{D}ealing with negatively weighted {E}vents in {DNN}-based
                      {LHC} {A}nalyses},
      reportid     = {PUBDB-2024-01548},
      year         = {2024},
      abstract     = {The recent decade has seen a growth of machine learning
                      algorithms across all disciplines. In LHC physics, a
                      multitude of applications have been tested and - in
                      particular Deep Neural Networks (DNNs) - have been proven to
                      be very effective in various usecases, for example in
                      particle tagging or for separating signal from background in
                      analyses. Since training data is primarily generated through
                      Monte-Carlo (MC) simulation, specific challenges can emerge
                      during DNN training due to partly negatively weighted
                      samples. MC simulations produce negative event weights in
                      the presence of destructive interference in the process or
                      in the case of next-to-leading order simulations with an
                      additive matching scheme. The negatively weighted training
                      data impair the DNN convergence. Therefore, the current
                      state of the art is to use reweighting methods that lead to
                      consistently positive weights. However this alters the input
                      distribution. We propose an alternative technique that is
                      interpretable, computationally efficient and does not affect
                      the input distribution. Furthermore, we show the method
                      employed on a hypothetical search for a beyond the standard
                      model heavy Higgs boson and discuss implications of negative
                      weights throughout DNN based analyses.},
      month         = {Mar},
      date          = {2024-03-04},
      organization  = {DPG Spring Meeting (German Physical
                       Society), Karlsruhe (Germany), 4 Mar
                       2024 - 8 Mar 2024},
      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/605866},
}