Record Details

Title:
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
Affiliation(s):
EuXFEL staff
Author group:
Instrument SQS
Topic:
Scientific area:
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.
Imprint:
2017
Journal Information:
Nature Communications, 8, 15461 (2017)
Related external records:
Language(s):
English


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 Record created 2017-06-06, last modified 2019-01-30

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