Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser
This work provides enhanced diagnostics for electron bunch tuning in scientific facilities like free-electron lasers, though it is incremental as it builds on existing diagnostic methods.
The researchers tackled the limited longitudinal information available for electron bunches in free-electron lasers by developing a neural network model that predicts longitudinal phase space images and coherent transition radiation spectra with high accuracy, enabling virtual diagnostics and improved online measurements.
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.