Paper  Title  Page 

FRXE1  Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for TimeVarying Systems  921 


Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This twopart tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive modelindependent feedback within physicsinformed ML architectures to make ML tools robust to timevariation (distribution shift) and to enable their use further beyond the span of the training data without relying on retraining.  
Slides FRXE1 [34.283 MB]  
DOI •  reference for this paper ※ doi:10.18429/JACoWNAPAC2022FRXE1  
About •  Received ※ 08 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 12 August 2022 — Issue date ※ 27 September 2022  
Cite •  reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
FRXE2 
Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for TimeVarying Systems  


Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This twopart tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive modelindependent feedback within physicsinformed ML architectures to make ML tools robust to timevariation (distribution shift) and to enable their use further beyond the span of the training data without relying on retraining.  
Cite •  reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  