Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power

TL;DR

This paper introduces a machine learning approach to predict the electron beam power profile in free-electron lasers, enabling single-shot pulse diagnostics without needing lasing-off measurements, thus improving FEL diagnostics.

Contribution

A novel machine learning model that predicts electron beam power profiles in FELs from lasing-on parameters, facilitating single-shot pulse reconstruction.

Findings

Model outperforms traditional batch calibration methods.

Validated predictions show high accuracy in electron beam power profiling.

Enables real-time, single-shot FEL pulse diagnostics.

Abstract

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.