A versatile framework for attitude tuning of beamlines at light source facilities

Area of Science:

• Scientific instrumentation
• Automation in research

Background:

• Automating experimental preparation steps like attitude tuning is crucial for efficiency at light sources.
• Current methods for attitude tuning can be time-consuming and require significant human effort.

Purpose of the Study:

• To develop a Mamba-based framework for automating attitude tuning in scientific experiments.
• To reduce the time and human effort involved in experimental setup and operation.

Main Methods:

• Created a Mamba-based attitude-tuning framework with flexible I/O ports.
• Integrated diverse evaluation functions and customizable optimization algorithms.
• Enabled seamless integration of machine learning (ML) and artificial intelligence (AI) technologies.

Main Results:

• Demonstrated framework utility through tuning a polycapillary lens and an X-ray emission spectrometer.
• Showcased specialized use with a Raman spectrometer and custom optimization algorithms.
• Developed a virtual-beamline mechanism for testing and training, including digital twins.

Conclusions:

• The Mamba-based framework effectively automates attitude tuning for various scientific instruments.
• The framework's flexibility supports diverse applications and facilitates human-in-the-loop control via CLIs and GUIs.
• The virtual-beamline mechanism enhances development and user training for experimental setups.