A general Bayesian algorithm for the autonomous alignment of beamlines.

Area of Science:

• Physics and Engineering
• Accelerator Science
• Computational Optimization

Background:

• Beamline alignment is a complex, high-dimensional optimization challenge.
• Current methods are time-consuming and may not find optimal configurations.
• Correlated and nonlinear dynamics of optical elements complicate alignment.

Purpose of the Study:

• To develop and implement an autonomous Bayesian optimization framework for efficient beamline alignment.
• To demonstrate the framework's ability to learn beamline dynamics online.
• To address practical challenges in multi-objective Bayesian optimization for beamline applications.

Main Methods:

• Formulation of Bayesian inference and Gaussian process models for multi-objective Bayesian optimization.
• Implementation of a general Bayesian optimization framework adaptable to specific beamline alignment tasks.
• Online hyperparameter fitting for rapid learning of beamline dynamics.

Main Results:

• Successful application of the framework to four distinct experimental beamline alignment problems (X-ray and electron beams).
• Demonstrated rapid online learning of beamline dynamics without prior information.
• Benchmarking against a simulated digital twin confirmed framework efficiency.

Conclusions:

• The presented Bayesian optimization framework offers a unified and efficient approach to beamline alignment.
• The method significantly reduces diagnostic time and improves beam quality.
• Potential for widespread adoption across synchrotron facilities for standardized alignment.