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@INPROCEEDINGS{Jafarinia:582756,
      author       = {Jafarinia, Farzad and Grech, Christian and Guetg, Marc and
                      Geloni, Gianluca},
      title        = {{S}imulation {S}tudy on a {V}irtual {D}iagnostics {C}oncept
                      for {X}-{R}ay {P}ulse {C}haracterisation},
      address      = {[Geneva]},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2023-02146},
      isbn         = {978-3-95450-231-8},
      pages        = {1813-1815},
      year         = {2023},
      note         = {Literaturangaben;},
      comment      = {[Ebook] IPAC'23 : 14th International Particle Accelerator
                      Conference, 7-12 May 2023, Venice, Italy : proceedings /
                      hosting institutions: INFN, Elettra Sincotrone Trieste ,
                      [Geneva] : JACoW Publishing, [2023],},
      booktitle     = {[Ebook] IPAC'23 : 14th International
                       Particle Accelerator Conference, 7-12
                       May 2023, Venice, Italy : proceedings /
                       hosting institutions: INFN, Elettra
                       Sincotrone Trieste , [Geneva] : JACoW
                       Publishing, [2023],},
      abstract     = {In this study we investigate simulation results for a
                      virtual diagnostics concept that is planned for the SASE1
                      beam-line at the European XFEL. These virtual diagnostics
                      will be used to predict photon beam properties like pointing
                      and divergence. We first use the GENESIS simulation
                      framework to compute different lasing conditions in the
                      undulator beamline, and then use Artificial Neural Networks
                      (ANN) to predict the pulse properties. The final model will
                      be able to estimate X-ray pulse characteristics based on
                      properties like electron beam trajectories inside the
                      undulator sections along with other diagnostics data. This
                      study will provide insight towards the development of online
                      virtual diagnostics in the real machine.},
      month         = {May},
      date          = {2023-05-07},
      organization  = {14th International Particle
                       Accelerator Conference, Venice (Italy),
                       7 May 2023 - 12 May 2023},
      cin          = {MXL},
      cid          = {I:(DE-H253)MXL-20160301},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      6G13 - Accelerator of European XFEL (POF4-6G13)},
      pid          = {G:(DE-HGF)POF4-621 / G:(DE-HGF)POF4-6G13},
      experiment   = {EXP:(DE-H253)XFEL-Exp-20150101 /
                      EXP:(DE-H253)XFEL(machine)-20150101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACoW-IPAC2023-TUPL021},
      url          = {https://bib-pubdb1.desy.de/record/582756},
}