Dingel, K.; Otto, T.; Marder, L.; Funke, L.; Held, A.; Savio, S.; Hans, A.; Hartmann, G.; Meier, D.; Viefhaus, J.; Sick, B.; Ehresmann, A.; Ilchen, M.; Helml, W.: Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses. Scientific Reports 12 (2022), p. 17809/1-14
10.1038/s41598-022-21646-x
Open Access Version
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.