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@ARTICLE{FerreiradeLima:622842,
      author       = {Ferreira de Lima, Danilo Enoque and Davtyan, Arman and
                      Laksman, Joakim and Gerasimova, Natalia and Maltezopoulos,
                      Theophilos and Liu, Jia and Schmidt, Philipp and Michelat,
                      Thomas and Mazza, Tommaso and Meyer, Michael and Grünert,
                      Jan and Gelisio, Luca},
      title        = {{M}achine-learning-enhanced automatic spectral
                      characterization of x-ray pulses from a free-electron laser},
      journal      = {Communications Physics},
      volume       = {7},
      number       = {1},
      issn         = {2399-3650},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {PUBDB-2025-00522},
      pages        = {400},
      year         = {2024},
      abstract     = {A reliable characterization of x-ray pulses is critical to
                      optimally exploit advanced photon sources, such as
                      free-electron lasers. In this paper, we present a method
                      based on machine learning, the virtual spectrometer, that
                      improves the resolution of non-invasive spectral diagnostics
                      at the European XFEL by up to $40\%,$ and significantly
                      increases its signal-to-noise ratio. This improves the
                      reliability of quasi-real-time monitoring, which is critical
                      to steer the experiment, as well as the interpretation of
                      experimental outcomes. Furthermore, the virtual spectrometer
                      streamlines and automates the calibration of the spectral
                      diagnostic device, which is otherwise a complex and
                      time-consuming task, by virtue of its underlying detection
                      principles. Additionally, the provision of robust quality
                      metrics and uncertainties enable a transparent and reliable
                      validation of the tool during its operation. A complete
                      characterization of the virtual spectrometer under a diverse
                      set of experimental and simulated conditions is provided in
                      the manuscript, detailing advantages and limits, as well as
                      its robustness with respect to the different test cases.},
      cin          = {$XFEL_DO_DD_DA$},
      ddc          = {530},
      cid          = {$I:(DE-H253)XFEL_DO_DD_DA-20210408$},
      pnm          = {6G13 - Accelerator of European XFEL (POF4-6G13) /
                      DIGIPREDICT - Edge AI-deployed DIGItal Twins for PREDICTing
                      disease progression and need for early intervention in
                      infectious and cardiovascular diseases beyond COVID-19
                      (101017915) / NETCO-PD - NETCO-PD: 14 experienced
                      researchers in network science for Europe (101034253)},
      pid          = {G:(DE-HGF)POF4-6G13 / G:(EU-Grant)101017915 /
                      G:(EU-Grant)101034253},
      experiment   = {EXP:(DE-H253)XFEL-Exp-20150101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001380084800001},
      doi          = {10.1038/s42005-024-01900-6},
      url          = {https://bib-pubdb1.desy.de/record/622842},
}