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@ARTICLE{Wolf:627945,
      author       = {Wolf, Moritz and Stietz, Lars Olaf and Connor, Patrick and
                      Schleper, Peter and Bein, Samuel},
      title        = {{F}ast {P}erfekt: {R}egression-based refinement of fast
                      simulation},
      journal      = {SciPost Physics Core},
      volume       = {8},
      number       = {1},
      issn         = {2666-9366},
      address      = {Amsterdam},
      publisher    = {SciPost Foundation},
      reportid     = {PUBDB-2025-01693},
      pages        = {021},
      year         = {2025},
      abstract     = {The availability of precise and accurate simulation is a
                      limiting factor for interpreting and forecasting data in
                      many fields of science and engineering. Often, one or more
                      distinct simulation software applications are developed,
                      each with a relative advantage in accuracy or speed. The
                      quality of insights extracted from the data stands to
                      increase if the accuracy of faster, more economical
                      simulation could be improved to parity or near parity with
                      more resource-intensive but accurate simulation. We present
                      Fast Perfekt, a machine-learned regression to refine the
                      output of fast simulation that employs residual neural
                      networks. A deterministic morphing model is trained using a
                      unique schedule that makes use of the ensemble loss function
                      MMD, with the option of an additional pair-based loss
                      function such as the MSE. We explore this methodology in the
                      context of an abstract analytical model and in terms of a
                      realistic particle physics application featuring jet
                      properties in hadron collisions at the CERN Large Hadron
                      Collider. The refinement makes maximum use of existing
                      domain knowledge, and introduces minimal computational
                      overhead to production.},
      pnm          = {HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$},
      pid          = {$G:(DE-HGF)2019_IVF-HIDSS-0002$},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21468/SciPostPhysCore.8.1.021},
      url          = {https://bib-pubdb1.desy.de/record/627945},
}