Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser

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. A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. The authors present a method based on machine learning which improves the resolution and signal-to-noise ratio of the non-invasive spectral diagnostics available at European XFEL, and streamlines its operation.