Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses
This work addresses a diagnostic bottleneck for XFEL users in ultrafast science, enabling more controllable light sources for studying electron dynamics, but it is incremental as it builds on an existing technique with AI improvements.
The authors tackled the problem of characterizing ultrashort X-ray free-electron laser pulses at the attosecond frontier, which is crucial for precise metrology in fields like single-molecule imaging, by using convolutional neural networks to enhance the photoelectron angular streaking technique, enabling routine diagnostics at high-repetition-rate XFELs and improving scientific accessibility.
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.