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Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

Measurements and analysis of orbit response matrix have been providing for decades a formidable tool in the detection of linear lattice imperfections and their correction. Basically all storage-ring-based synchrotron light sources across the world make routinely use of this technique in their daily operation, reaching in some cases a correction of linear optics down to 1% beta beating and 0.1% coupling. During the design phase of a new storage ring it is also applied in simulations for the evaluation of magnetic and mechanical tolerances. However, this technique is known for its intrinsic slowness compared to other methods based on turn-by-turn beam position data, both in the measurement and in the data analysis. In this paper analytic formulas are derived and discussed that shall greatly speed up this second part. The mathematical formalism based on the Lie algebra and the resonance driving terms is extended to the off-momentum regime and explicit analytic formulas for the evaluation of chromatic functions from lattice parameters are also derived. The robustness of these formulas, which are linear in the magnet strengths, is tested with different lattice configurations.