Artificial Intelligence for Enhancing Operation and Exploitation of X-ray Free-Electron Lasers (AIOPs4XFEL)

Modern X-ray free-electron lasers (XFELs) have transformed how scientists study molecular and material structures. Their unique beam properties are crucial for experiments, but optimizing these features, especially advanced ones such as the wavefront shape, is a complex and time-consuming endeavor. Moreover, the lack of near-real-time feedback from experiments to the XFEL machine prevents immediate beam adjustments, resulting in inefficient use of valuable experimental time and limiting XFEL's full scientific potential.

In this project, AI4Ops@XFEL, we propose optimizing X-ray free-electron laser (XFEL) operation through AI-driven real-time feedback, addressing critical challenges in optimizing beam parameters and maximizing data quality. By integrating machine learning into experimental diagnostics, this project aims to unlock AI-based control over XFEL beam properties, building the foundation for significantly enhancing experiment quality.