Datum
2022-10-24Autor
Dingel, KristinaOtto, ThorstenMarder, LutzFunke, LarsHeld, ArneSavio, SaraHans, AndreasHartmann, GregorMeier, DavidViefhaus, JensSick, BernhardEhresmann, ArnoIlchen, MarkusHelml, WolframMetadata
Zur Langanzeige
Aufsatz
Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses
Zusammenfassung
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.
Zitierform
In: Scientific Reports Volume 12 (2022-10-24) eissn:2045-2322Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202304287915,
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},
title={Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses},
journal={Scientific Reports},
year={2022}
}
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2023-04-28T14:38:02Z 2023-04-28T14:38:02Z 2022-10-24 doi:10.17170/kobra-202304287915 http://hdl.handle.net/123456789/14640 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ Applied physics Characterization and analytical techniques Computer science Statistics 530 Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses Aufsatz 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. open access Dingel, Kristina Otto, Thorsten Marder, Lutz Funke, Lars Held, Arne Savio, Sara Hans, Andreas Hartmann, Gregor Meier, David Viefhaus, Jens Sick, Bernhard Ehresmann, Arno Ilchen, Markus Helml, Wolfram 14 Seiten doi:10.1038/s41598-022-21646-x Angewandte Physik Röntgenlaser Ultrakurzzeitlaser Künstliche Intelligenz publishedVersion eissn:2045-2322 Scientific Reports Volume 12 false 17809
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