Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Summary, in English

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.

• Author: A. Sanchez-Gonzalez; P. Micaelli; C. Olivier; T. R. Barillot; M. Ilchen; A. Lutman; A. Marinelli; T Maxwell; A. Achner; M. Agåker; N. Berrah; C. Bostedt; J. D. Bozek; J. Buck; P. H. Bucksbaum; S. Carron Montero; B. Cooper; J. P. Cryan; M Dong; R Feifel; L. J. Frasinski; H. Fukuzawa; A. Galler; G. Hartmann; Nils Hartmann; W. Helml; A. S. Johnson; A. Knie; A. O. Lindahl; J. Liu; K. Motomura; M. Mucke; Caroline O'Grady; J E Rubensson; E. R. Simpson; R J Squibb; C. Såthe; K. Ueda; M. Vacher; D. J. Walke; V. Zhaunerchyk; R. N. Coffee; J. P Marangos
• Department/s: MAX IV Laboratory
• Publishing year: 2017-06-05
• Language: English
• Publication/Series: Nature Communications
• Volume: 8
• Links: http://dx.doi.org/10.1038/ncomms15461
• Document type: Journal article
• Publisher: Nature Publishing Group
• Topic: Atom and Molecular Physics and Optics
• Status: Published
• ISBN/ISSN/Other: ISSN: 2041-1723