Speaker
Description
Achieving precise and real-time diagnostics of electron beam characteristics is critical for enhancing the performance of ultrafast electron diffraction (UED) and electron microscopy (UEM) techniques. Key parameters such as bunch size, emittance, energy spread, and spatial pointing jitter directly influence the quality and accuracy of experimental results. Traditional diagnostic methods often lack the ability to provide continuous, real-time, and non-intrusive monitoring, limiting their effectiveness. This work presents a machine learning (ML)-based approach that utilizes a small dataset of known beam parameters in combination with real-time diffraction image data recorded during experiments to predict electron beam characteristics for each run. This approach enables continuous optimization of beam stability without interfering with the experiment and facilitates real-time updates to UED parameters during data collection. As a result, it significantly improves the precision, reliability, and overall performance of UED and UEM experiments.
| Region represented | Asia |
|---|---|
| Paper preparation format | Word |
