Machine learning applied to single-shot x-ray diagnostics in an XFEL
This provides a solution for real-time pulse characterization at high-repetition-rate XFELs, enabling better data sorting, though it is incremental as it adapts existing ML tools to a specific domain.
The researchers tackled the problem of characterizing fluctuating x-ray pulses in XFELs by applying machine learning to predict pulse properties, achieving mean errors below 0.3 eV for photon energy and 1.6 fs for pulse delay, with 97% agreement in spectral shape prediction.
X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate.