Journal Article

PUBDB-2025-00522

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser

Ferreira de Lima, D. E. (Corresponding author)XFEL.EU* ; Davtyan, A. ; Laksman, J. ; Gerasimova, N. ; Maltezopoulos, T. ; Liu, J.XFEL.EU* ; Schmidt, P. ; Michelat, T. ; Mazza, T. ; Meyer, M. ; Grünert, J. ; Gelisio, L.

2024
Springer Nature London

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Please use a persistent id in citations: doi:10.1038/s42005-024-01900-6  doi:10.3204/PUBDB-2025-00522

Abstract: A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. In this paper, we present a method based on machine learning, the virtual spectrometer, that improves the resolution of non-invasive spectral diagnostics at the European XFEL by up to 40%, and significantly increases its signal-to-noise ratio. This improves the reliability of quasi-real-time monitoring, which is critical to steer the experiment, as well as the interpretation of experimental outcomes. Furthermore, the virtual spectrometer streamlines and automates the calibration of the spectral diagnostic device, which is otherwise a complex and time-consuming task, by virtue of its underlying detection principles. Additionally, the provision of robust quality metrics and uncertainties enable a transparent and reliable validation of the tool during its operation. A complete characterization of the virtual spectrometer under a diverse set of experimental and simulated conditions is provided in the manuscript, detailing advantages and limits, as well as its robustness with respect to the different test cases.

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Contributing Institute(s):

1. Data Analysis (XFEL_DO_DD_DA)

Research Program(s):

1. 6G13 - Accelerator of European XFEL (POF4-6G13) (POF4-6G13)
2. DIGIPREDICT - Edge AI-deployed DIGItal Twins for PREDICTing disease progression and need for early intervention in infectious and cardiovascular diseases beyond COVID-19 (101017915) (101017915)
3. NETCO-PD - NETCO-PD: 14 experienced researchers in network science for Europe (101034253) (101034253)

Experiment(s):

1. Experiments at XFEL

Appears in the scientific report 2024

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