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Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
Imperial Coll London, Dept Phys, London SW7 2AZ, England..
Imperial Coll London, Dept Phys, London SW7 2AZ, England..
Imperial Coll London, Dept Phys, London SW7 2AZ, England..
Imperial Coll London, Dept Phys, London SW7 2AZ, England..
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2017 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 8, article id 15461Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2017. Vol. 8, article id 15461
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Condensed Matter Physics
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URN: urn:nbn:se:uu:diva-327226DOI: 10.1038/ncomms15461ISI: 000402745000001PubMedID: 28580940OAI: oai:DiVA.org:uu-327226DiVA, id: diva2:1136026
Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2017-11-29Bibliographically approved

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Agåker, M.Dong, M.Mucke, M.Rubensson, J-E.Vacher, M.

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