Particle Acceleration and Detection

This book begins with an overview of the RF control concepts and strategies. It then introduces RF system models for optimizing the system parameters to satisfy beam requirements and for controller design. In addition to systematically discussing the RF field control algorithms, it presents typical architecture and algorithms for RF signal detection and actuation. Further, the book addresses the analysis of the noise and nonlinearity in LLRF systems to provide a better understanding of the performance of the RF control system and to specify the performance requirements for different parts of the RF system. 

Today, accelerators require increased RF stability and more complex operation scenarios, such as providing beam for different beam lines with various parameters, and as a result LLRF systems are becoming more critical and complex. This means that LLRF system developers need have extensive knowledge of the entire accelerator complex and a wide range of other areas, including RF and digital signal processing, noise analysis, accelerator physics and systems engineering.

Providing a comprehensive introduction to the basic theories, algorithms and technologies, this book enables LLRF system developers to systematically gain the knowledge required to specify, design and implement LLRF systems and integrate them with beam acceleration. It is intended for graduate students, professional engineers and researchers in accelerator physics.

This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.

In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems.