Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power
This work addresses a crucial diagnostic hurdle for free-electron laser operations, promising to enhance capabilities without requiring repeated measurements, though it appears incremental as it builds on existing virtual pulse reconstruction tools.
The paper tackled the problem of measuring free-electron laser pulse power for single electron bunches, which is impossible with traditional methods, by developing a machine learning model that predicts temporal power profiles from machine parameters, showing superior predictions compared to state-of-the-art batch calibrations.
Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context of free-electron lasers (FELs), it is fundamentally impossible to measure the lasing-on and lasingoff electron power profiles for a single electron bunch. This is a crucial hurdle in the exact reconstruction of the photon pulse profile. To overcome this hurdle, we developed a machine learning model that predicts the temporal power profile of the electron bunch in the lasing-off regime using machine parameters that can be obtained when lasing is on. The model was statistically validated and showed superior predictions compared to the state-of-the-art batch calibrations. The work we present here is a critical element for a virtual pulse reconstruction diagnostic (VPRD) tool designed to reconstruct the power profile of individual photon pulses without requiring repeated measurements in the lasing-off regime. This promises to significantly enhance the diagnostic capabilities in FELs at large.