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

Journal article
Authors A. Sanchez-Gonzalez
P. Micaelli
C. Olivier
T. R. Barillot
M. Ilchen
A. A. Lutman
A. Marinelli
T. Maxwell
A. Achner
M. Agaker
N. Berrah
C. Bostedt
J. D. Bozek
J. Buck
P. H. Bucksbaum
S. C. Montero
B. Cooper
J. P. Cryan
M. Dong
Raimund Feifel
L. J. Frasinski
H. Fukuzawa
A. Galler
G. Hartmann
N. Hartmann
W. Helml
A. S. Johnson
A. Knie
Anton Lindahl
J. Liu
K. Motomura
M. Mucke
C. O'Grady
J. E. Rubensson
E. R. Simpson
Richard J. Squibb
C. Sathe
K. Ueda
M. Vacher
D. J. Walke
Vitali Zhaunerchyk
R. N. Coffee
J. P. Marangos
Published in Nature Communications
Volume 8
ISSN 2041-1723
Publication year 2017
Published at Department of Physics (GU)
Language en
Links doi.org/10.1038/ncomms15461
https://gup.ub.gu.se/file/206945
Keywords neural-networks, time, physics, spectroscopy, radiation, dynamics, Science & Technology - Other Topics
Subject categories Physical Sciences

Abstract

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.

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