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

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Title: Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
Authors: Sanchez Gonzalez, A
Micaelli, P
Olivier, C
Barillot, TR
Ilchen, I
Lutman, AA
Marinelli, A
Maxwell, T
Achner, A
Agåker, M
Berrah, N
Bostedt, C
Bozek, JD
Buck, J
Bucksbaum, PH
Carron Montero, S
Cooper, B
Cryan, JP
Dong, M
Feifel, R
Frasinski, LJ
Fukuzawa, H
Galler, A
Hartmann, G
Hartmann, N
Helml, W
Johnson, AS
Knie, A
Lindahl, AO
Liu, J
Motomura, K
Mucke, M
O'Grady, C
Rubensson, J-E
Simpson, ER
Squibb, RJ
Såthe, C
Ueda, K
Vacher, M
Walke, DJ
Zhaunerchyk, V
Coffee, RN
Marangos, JP
Item Type: Journal Article
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.
Issue Date: 5-Jun-2017
Date of Acceptance: 30-Mar-2017
URI: http://hdl.handle.net/10044/1/46152
DOI: https://dx.doi.org/10.1038/ncomms15461
ISSN: 2041-1723
Publisher: Nature Publishing Group
Journal / Book Title: Nature Communications
Volume: 8
Copyright Statement: © The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/I032517/1
290467
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
NEURAL-NETWORKS
FEMTOSECOND
TIME
PHYSICS
PHOTOABSORPTION
SPECTROSCOPY
RADIATION
DYNAMICS
SPECTRA
physics.data-an
physics.data-an
physics.acc-ph
stat.ML
MD Multidisciplinary
Publication Status: Published
Article Number: 15461
Appears in Collections:Quantum Optics and Laser Science
Physics



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