Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses

• URL: http://arxiv.org/abs/2108.13979v1
• Date: Tue, 31 Aug 2021 17:04:47 GMT
• Title: Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses
• Authors: Kristina Dingel, Thorsten Otto, Lutz Marder, Lars Funke, Arne Held, Sara Savio, Andreas Hans, Gregor Hartmann, David Meier, Jens Viefhaus, Bernhard Sick, Arno Ehresmann, Markus Ilchen, Wolfram Helml
• Abstract summary: Photoelectron angular streaking has successfully retrieved the exact time-energy structure of XFEL pulses on a single-shot basis. We show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics at XFELs.
• Score: 0.9743237458721049
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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