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.
