期刊
NATURE COMMUNICATIONS
卷 8, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/ncomms15461
关键词
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资金
- Engineering and Physical Sciences Research Council (UK) (EPSRC) [EP/I032517/1]
- European Research Council (ERC) ASTEX Project [290467]
- Science and Technology Facilities Council (STFC)
- X-ray Free Electron Laser Utilization Research Project
- X-ray Free Electron Laser Priority Strategy Program of the Ministry of Education, Culture, Sports, Science and Technology of Japan
- Swedish Research Council (VR)
- Knut and Alice Wallenberg Foundation (KAW), Sweden
- Stockholm-Uppsala Center for Free Electron Laser Research, Sweden
- VW foundation within a Peter Paul Ewald-Fellowship
- Marie Curie International Outgoing Fellowship
- Hesse excellence initiative LOEWE within the focus program ELCH
- DOE, Sc, BES, Division of Chemical Sciences, Geosciences and Biosciences [DE-SC0012376]
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]
- Engineering and Physical Sciences Research Council [EP/I032517/1, 1227506] Funding Source: researchfish
- EPSRC [EP/I032517/1] Funding Source: UKRI
- U.S. Department of Energy (DOE) [DE-SC0012376] Funding Source: U.S. Department of Energy (DOE)
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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