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Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning

Abstract:
X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41598-024-56782-z

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Institution:
University of Oxford
Role:
Author
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Role:
Author
ORCID:
0000-0001-8221-7382
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Author
ORCID:
0000-0001-9308-4925
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Author
ORCID:
0000-0001-8213-4368
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Role:
Author
ORCID:
0000-0003-3873-8804


Publisher:
Nature Research
Journal:
Scientific Reports More from this journal
Volume:
14
Issue:
1
Pages:
7267-7267
Article number:
7267
Publication date:
2024-03-27
DOI:
EISSN:
2045-2322
ISSN:
2045-2322


Language:
English
Keywords:
Pubs id:
2347559
Local pid:
pubs:2347559
Source identifiers:
W4393224524
Deposit date:
2025-12-08
ARK identifier:
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