@article{dingel2022artificial,
  abstract = {X-ray free-electron lasers (XFELs) as the world’s brightest light sources provide ultrashort X-ray pulses
with a duration typically in the order of femtoseconds. Recently, they have approached and entered
the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear
and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs
toward well-controllable light sources for precise metrology of ultrafast processes has been, however,
hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In
this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven
how to non-destructively retrieve the exact time–energy structure of XFEL pulses on a single-shot
basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here
show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics
even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all
related disciplines.},
  author = {Dingel, Kristina and Otto, Thorsten and Marder, Lutz and Funke, Lars and Held, Arne and Savio, Sara and Hans, Andreas and Hartmann, Gregor and Meier, David and Viefhaus, Jens and Sick, Bernhard and Ehresmann, Arno and Ilchen, Markus and Helml, Wolfram},
  doi = {10.1038/s41598-022-21646-x},
  interhash = {161048047fb287f2d82b522d409fd05f},
  intrahash = {372e322e577a23689c7b0bd48b88ce8d},
  journal = {Scientific Reports},
  number = 1,
  pages = {1--14},
  publisher = {Nature Publishing Group},
  title = {Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses},
  url = {https://www.nature.com/articles/s41598-022-21646-x},
  volume = 12,
  year = 2022
}

@article{DOM+21,
  abstract = {X-ray free-electron lasers (XFELs) as the world`s most brilliant light sources provide ultrashort X-ray pulses with durations typically on the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena like localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes was, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence algorithms, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs, thus enhancing and refining their scientific access in all related disciplines.},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210813979D},
  archiveprefix = {arXiv},
  author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram},
  eid = {arXiv:2108.13979},
  eprint = {2108.13979},
  interhash = {c803192ee609104c0af8500c632d5d01},
  intrahash = {3904c6bd556676ba2223dbe50000ecbf},
  journal = {arXiv e-prints},
  month = aug,
  pages = {arXiv:2108.13979},
  primaryclass = {physics.data-an},
  title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}},
  year = 2021
}