Sanchez-Gonzalez, A;
Micaelli, P;
Olivier, C;
Barillot, TR;
Ilchen, M;
Lutman, AA;
Marinelli, A;
... Marangos, JP; + view all
(2017)
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning.
Nature Communications
, 8
, Article 15461. 10.1038/ncomms15461.
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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: | Article |
|---|---|
| Title: | Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/ncomms15461 |
| Publisher version: | http://doi.org/10.1038/ncomms15461 |
| Language: | English |
| Additional information: | Copyright © 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/ |
| Keywords: | Ultrafast photonics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10045922 |
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