Smart X-ray beam position monitor system using artificial-intelligence methods for the Advanced Photon Source...

Area of Science:

• Synchrotron Radiation Science
• Particle Accelerator Technology
• Instrumentation and Measurement

Background:

• Conventional photoemission-type X-ray beam position monitors (XBPMs) at the Advanced Photon Source (APS) face operational challenges.
• Signal contamination from bending-magnet sources and sensitivity to insertion-device (ID) gap variations degrade XBPM performance.
• User-induced ID gap changes exacerbate signal contamination, impacting data reliability.

Purpose of the Study:

• To develop and present a novel smart XBPM system for APS insertion-device beamline front ends.
• To address the limitations of conventional XBPMs, specifically signal contamination and gap sensitivity.
• To introduce an AI-driven system with self-learning and self-calibration capabilities.

Main Methods:

• Implementation of a smart XBPM system featuring a high-speed digital signal processor.
• Development of new software incorporating artificial intelligence (AI) algorithms.
• Testing and evaluation of the smart XBPM system's performance in real-world operational scenarios.

Main Results:

• The smart XBPM system demonstrates enhanced capabilities in monitoring X-ray beam position.
• The AI-based software provides self-learning and self-calibration, improving system adaptability.
• Initial test results indicate successful mitigation of contamination and gap variation issues.

Conclusions:

• The developed smart XBPM system offers a significant improvement over conventional methods for beam position monitoring at synchrotron facilities.
• AI integration in XBPMs provides a robust solution for maintaining high accuracy despite operational complexities.
• The system is poised to enhance the reliability and efficiency of experiments conducted at insertion-device beamlines.