Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses

TL;DR

This paper demonstrates how artificial intelligence, specifically convolutional neural networks, can be used to improve the routine, non-destructive characterization of ultrashort X-ray pulses from free-electron lasers, enabling better ultrafast science.

Contribution

The study introduces AI-driven analysis to advance photoelectron angular streaking for real-time XFEL pulse diagnostics at high repetition rates.

Findings

AI enhances pulse characterization accuracy

Enables routine, non-destructive diagnostics

Applicable to high-repetition-rate XFELs

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.