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Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses.
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.
Afiliación
  • Dingel K; Intelligent Embedded Systems, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany. kristina.dingel@uni-kassel.de.
  • Otto T; Artificial Intelligence Methods for Experiment Design (AIM-ED), Joint Lab Helmholtzzentrum für Materialien und Energie, Berlin (HZB) and University of Kassel, Berlin, Germany. kristina.dingel@uni-kassel.de.
  • Marder L; Intelligent Embedded Systems, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany.
  • Funke L; Artificial Intelligence Methods for Experiment Design (AIM-ED), Joint Lab Helmholtzzentrum für Materialien und Energie, Berlin (HZB) and University of Kassel, Berlin, Germany.
  • Held A; Institute for Physics and CINSaT, University of Kassel, Heinrich-Plett-Straße 40, 34132, Kassel, Germany.
  • Savio S; Fakultät Physik, Technische Universität Dortmund, Maria-Goeppert-Mayer-Straße 2, 44227, Dortmund, Germany.
  • Hans A; Fakultät Physik, Technische Universität Dortmund, Maria-Goeppert-Mayer-Straße 2, 44227, Dortmund, Germany.
  • Hartmann G; Fakultät Physik, Technische Universität Dortmund, Maria-Goeppert-Mayer-Straße 2, 44227, Dortmund, Germany.
  • Meier D; Artificial Intelligence Methods for Experiment Design (AIM-ED), Joint Lab Helmholtzzentrum für Materialien und Energie, Berlin (HZB) and University of Kassel, Berlin, Germany.
  • Viefhaus J; Institute for Physics and CINSaT, University of Kassel, Heinrich-Plett-Straße 40, 34132, Kassel, Germany.
  • Sick B; Artificial Intelligence Methods for Experiment Design (AIM-ED), Joint Lab Helmholtzzentrum für Materialien und Energie, Berlin (HZB) and University of Kassel, Berlin, Germany.
  • Ehresmann A; Helmholtz-Zentrum Berlin für Materialien und Energie, Hahn-Meitner-Platz 1, 14109, Berlin, Germany.
  • Ilchen M; Intelligent Embedded Systems, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany.
  • Helml W; Artificial Intelligence Methods for Experiment Design (AIM-ED), Joint Lab Helmholtzzentrum für Materialien und Energie, Berlin (HZB) and University of Kassel, Berlin, Germany.
Sci Rep ; 12(1): 17809, 2022 Oct 24.
Article en En | MEDLINE | ID: mdl-36280680
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.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Alemania
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