Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

TL;DR

This paper introduces a neural network model that virtually reconstructs longitudinal phase space diagnostics in a free-electron laser, enhancing real-time measurement accuracy and enabling efficient testing of control algorithms.

Contribution

It presents a novel AI-based approach to combine virtual and real diagnostics, significantly improving online longitudinal property measurements of electron bunches.

Findings

High-accuracy predictions of LPS images and spectra for diverse bunch shapes.

Effective enhancement of CTR spectrometer measurements through combined predictions.

Demonstration at FLASH facility validates the model's robustness and utility.

Abstract

Longitudinal properties of electron bunches are critical for the performance of a wide range of scientific facilities. In a free-electron laser, for example, the existing diagnostics only provide very limited longitudinal information of the electron bunch during online tuning and optimization. We leverage the power of artificial intelligence to build a neural network model using experimental data, in order to bring the destructive longitudinal phase space (LPS) diagnostics online virtually and improve the existing current profile online diagnostics which uses a coherent transition radiation (CTR) spectrometer. The model can also serve as a digital twin of the real machine on which algorithms can be tested efficiently and effectively. We demonstrate at the FLASH facility that the encoder-decoder model with more than one decoder can make highly accurate predictions of megapixel LPS images and coherent transition radiation spectra concurrently for electron bunches in a bunch train with broad ranges of LPS shapes and peak currents, which are obtained by scanning all the major control knobs for LPS manipulation. Furthermore, we propose a way to significantly improve the CTR spectrometer online measurement by combining the predicted and measured spectra. Our work showcases how to combine virtual and real diagnostics in order to provide heterogeneous and reliable mixed diagnostics for scientific facilities.