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@ARTICLE{Breckwoldt:491341,
      author       = {Breckwoldt, Niels and Son, Sang-Kil and Mazza, Tommaso and
                      Roerig, Aljoscha and Boll, Rebecca and Meyer, Michael and
                      Laforge, Aaron and Mishra, Debadarshini and Berrah, Nora and
                      Santra, Robin},
      title        = {{M}achine-learning calibration of intense x-ray
                      free-electron-laser pulses using {B}ayesian optimization},
      journal      = {Physical review research},
      volume       = {5},
      number       = {2},
      issn         = {2643-1564},
      address      = {College Park, MD},
      publisher    = {APS},
      reportid     = {PUBDB-2023-00096},
      pages        = {023114},
      year         = {2023},
      abstract     = {X-ray free-electron lasers (XFELs) have brought new ways to
                      probe and manipulate atomic and molecular dynamics with
                      unprecedented spatial and temporal resolutions. A
                      quantitative comparison of experimental results with their
                      simulated theoretical counterpart, however, generally
                      requires a precise characterization of the spatial and
                      temporal x-ray pulse profile, providing a nonuniform photon
                      distribution. The determination of the pulse profile
                      constitutes a major, yet inevitable, challenge. Here, we
                      propose a calibration scheme for intense XFEL pulses
                      utilizing a set of experimental charge-state distributions
                      of light noble gas atoms at a series of pulse energies in
                      combination with first-principles simulations of the
                      underlying atomic x-ray multiphoton ionization dynamics. The
                      calibration builds on Bayesian optimization, which is a
                      powerful, machine-learning-based tool particularly well
                      suited for computationally expensive numerical optimization.
                      We demonstrate the presented scheme to calibrate the pulse
                      duration as well as the spatial fluence distribution profile
                      of XFEL pulses. Our proposed method can serve as a
                      comprehensive tool for characterizing ultraintense and
                      ultrafast x-ray pulses.},
      cin          = {CFEL-DESYT / FS-CFEL-3 / $XFEL_E2_SQS$},
      ddc          = {530},
      cid          = {I:(DE-H253)CFEL-DESYT-20160930 /
                      I:(DE-H253)FS-CFEL-3-20120731 /
                      $I:(DE-H253)XFEL_E2_SQS-20210408$},
      pnm          = {631 - Matter – Dynamics, Mechanisms and Control
                      (POF4-631) / DFG project 390715994 - EXC 2056: CUI: Advanced
                      Imaging of Matter (390715994) / DFG project 194651731 - EXC
                      1074: Hamburger Zentrum für ultraschnelle Beobachtung
                      (CUI): Struktur, Dynamik und Kontrolle von Materie auf
                      atomarer Skala (194651731) / DFG project 170620586 - SFB
                      925: Licht-induzierte Dynamik und Kontrolle korrelierter
                      Quantensysteme (170620586)},
      pid          = {G:(DE-HGF)POF4-631 / G:(GEPRIS)390715994 /
                      G:(GEPRIS)194651731 / G:(GEPRIS)170620586},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000998007900003},
      doi          = {10.1103/PhysRevResearch.5.023114},
      url          = {https://bib-pubdb1.desy.de/record/491341},
}