%0 Journal Article
%1 DOM+21
%A Dingel, Kristina
%A Otto, Thorsten
%A Marder, Lutz
%A Funke, Lars
%A Held, Arne
%A Savio, Sara
%A Hans, Andreas
%A Hartmann, Gregor
%A Meier, David
%A Viefhaus, Jens
%A Sick, Bernhard
%A Ehresmann, Arno
%A Ilchen, Markus
%A Helml, Wolfram
%D 2021
%J arXiv e-prints
%K - Accelerator Analysis, Artificial Computer Data Intelligence, Optics Physics Physics, Probability, Science Statistics and isac-www
%P arXiv:2108.13979
%T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses
%X 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.