In:
Nature Communications, Springer Science and Business Media LLC, Vol. 8, No. 1 ( 2017-06-05)
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.
Type of Medium:
Online Resource
ISSN:
2041-1723
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2017
detail.hit.zdb_id:
2553671-0
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