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@ARTICLE{Butter:600225,
      author       = {Butter, Anja and Plehn, Tilman and Schumann, Steffen and
                      Badger, Simon and Caron, Sascha and Cranmer, Kyle and Di
                      Bello, Francesco Armando and Dreyer, Etienne and Forte,
                      Stefano and Ganguly, Sanmay and Gonçalves, Dorival and
                      Gross, Eilam and Heimel, Theo and Heinrich, Gudrun and
                      Heinrich, Lukas and Held, Alexander and Höche, Stefan and
                      Howard, Jessica N. and Ilten, Philip and Isaacson, Joshua
                      and Janßen, Timo and Jones, Stephen and Kado, Marumi and
                      Kagan, Michael and Kasieczka, Gregor and Kling, Felix and
                      Kraml, Sabine and Krause, Claudius and Krauss, Frank and
                      Kröninger, Kevin and Barman, Rahool Kumar and Luchmann,
                      Michel and Magerya, Vitaly and Maitre, Daniel and Malaescu,
                      Bogdan and Maltoni, Fabio and Martini, Till and Mattelaer,
                      Olivier and Nachman, Benjamin and Pitz, Sebastian and Rojo,
                      Juan and Schwartz, Matthew and Shih, David and Siegert,
                      Frank and Stegeman, Roy and Stienen, Bob and Thaler, Jesse
                      and Verheyen, Rob and Whiteson, Daniel and Winterhalder,
                      Ramon and Zupan, Jure},
      title        = {{M}achine learning and {LHC} event generation},
      journal      = {SciPost physics},
      volume       = {14},
      number       = {4},
      issn         = {2542-4653},
      address      = {Amsterdam},
      publisher    = {SciPost Foundation},
      reportid     = {PUBDB-2023-07799},
      pages        = {079},
      year         = {2023},
      note         = {Contribution to: Snowmass 2021},
      abstract     = {First-principle simulations are at the heart of the
                      high-energy physics research program. They link the vast
                      data output of multi-purpose detectors with fundamental
                      theory predictions and interpretation. This review
                      illustrates a wide range of applications of modern machine
                      learning to event generation and simulation-based inference,
                      including conceptional developments driven by the specific
                      requirements of particle physics. New ideas and tools
                      developed at the interface of particle physics and machine
                      learning will improve the speed and precision of forward
                      simulations, handle the complexity of collision data, and
                      enhance inference as an inverse simulation problem.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {Snowmass 2021, Seattle (United
                       States), 17 Jul 2022 - 26 Jul 2022},
      keywords     = {interface (INSPIRE) / interpretation of experiments: CERN
                      LHC Coll (INSPIRE)},
      cin          = {T},
      ddc          = {530},
      cid          = {I:(DE-H253)T-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / DFG
                      project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe
                      (390833306) / DFG project G:(GEPRIS)390900948 - EXC 2181:
                      STRUKTUREN: Emergenz in Natur, Mathematik und komplexen
                      Daten (390900948)},
      pid          = {G:(DE-HGF)POF4-611 / G:(GEPRIS)390833306 /
                      G:(GEPRIS)390900948},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)16},
      UT           = {WOS:000996367300018},
      doi          = {10.21468/SciPostPhys.14.4.079},
      url          = {https://bib-pubdb1.desy.de/record/600225},
}