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.
%0 Journal Article
%1 dingel2021ai
%A Dingel, Kristina
%A Otto, Thorsten
%A Marder, Lutz
%A Funke, Lars
%A Held, Arne
%A Savio, Sara
%A Hans, Andreas
%A Hartmann, Gregor
%A Meier, David
%A Viefhaus, Jens
%A Sick, Bernhard
%A Ehresmann, Arno
%A Ilchen, Markus
%A Helml, Wolfram
%D 2021
%J arXiv e-prints
%K - Accelerator Analysis, Artificial Computer Data Intelligence, Optics Physics Physics, Probability, Science Statistics and itegpub
%P arXiv:2108.13979
%T Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses
%X 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.
@article{dingel2021ai,
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.},
added-at = {2022-01-07T10:37:59.000+0100},
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210813979D},
archiveprefix = {arXiv},
author = {{Dingel}, Kristina and {Otto}, Thorsten and {Marder}, Lutz and {Funke}, Lars and {Held}, Arne and {Savio}, Sara and {Hans}, Andreas and {Hartmann}, Gregor and {Meier}, David and {Viefhaus}, Jens and {Sick}, Bernhard and {Ehresmann}, Arno and {Ilchen}, Markus and {Helml}, Wolfram},
biburl = {https://www.bibsonomy.org/bibtex/23904c6bd556676ba2223dbe50000ecbf/ies},
eid = {arXiv:2108.13979},
eprint = {2108.13979},
interhash = {c803192ee609104c0af8500c632d5d01},
intrahash = {3904c6bd556676ba2223dbe50000ecbf},
journal = {arXiv e-prints},
keywords = {- Accelerator Analysis, Artificial Computer Data Intelligence, Optics Physics Physics, Probability, Science Statistics and itegpub},
pages = {arXiv:2108.13979},
primaryclass = {physics.data-an},
timestamp = {2022-01-07T10:37:59.000+0100},
title = {{Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses}},
year = 2021
}