Der Artikel mit dem Titel Artificial intelligence for online characterization of ultrashort X‑ray free‑electron laser pulses wurde für die Veröffentlichung im Journal Scientific Reports der Springer Nature Gruppe akzeptiert. Autor:innen dieses Journals sind Kristina Dingel, Thorsten Otto, Lutz Marder, Lars Funke, Arne Held, Sara Savio, Andreas Hans, Gregor Hartmann, David Meier, Jens Viefhaus, Bernhard Sick, Arno Ehresmann, Markus Ilchen und Wolfram Helml. Der Inhalt des Artikels ist wie folgt:

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.