Beam Based Correction And Optimization For Accelerators
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Beam-based Correction and Optimization for Accelerators
This book provides systematic coverage of the beam-based techniques that accelerator physicists use to improve the performance of large particle accelerators, including synchrotrons and linacs. It begins by discussing the basic principles of accelerators, before exploring the various error sources in accelerators and their impact on the machine's performances. The book then demonstrates the latest developments of beam-based correction techniques that can be used to address such errors and covers the new and expanding area of beam-based optimization. This book is an ideal, accessible reference book for physicists working on accelerator design and operation, and for postgraduate studying accelerator physics. Features: Entirely self-contained, exploring the theoretic background, including algorithm descriptions, and providing application guidance Accompanied by source codes of the main algorithms and sample codes online Uses real-life accelerator problems to illustrate principles, enabling readers to apply techniques to their own problems Xiaobiao Huang is an accelerator physicist at the SLAC National Accelerator Laboratory at Stanford University, USA. He graduated from Tsinghua University with a Bachelor of Science in Physics and a Bachelor of Engineering in Computer Science in 1999. He earned a PhD in Accelerator Physics from Indiana University, Bloomington, Indiana, USA, in 2005. He spent three years on thesis research work at Fermi National Accelerator Laboratory from 2003-2005. He has worked at SLAC as a staff scientist since 2006. He became Accelerator Physics Group Leader of the SPEAR3 Division, Accelerator Directorate in 2015. His research work in accelerator physics ranges from beam dynamics, accelerator design, and accelerator modelling and simulation to beam based measurements, accelerator control, and accelerator optimization. He has taught several courses at US Particle Accelerator School (USPAS), including Beam Based Diagnostics, Accelerator Physics, Advanced Accelerator Physics, and Special Topics in Accelerator Physics.
Beam-based Correction and Optimization for Accelerators
This book provides systematic coverage of the beam-based techniques that accelerator physicists use to improve the performance of large particle accelerators, including synchrotrons and linacs. It begins by discussing the basic principles of accelerators, before exploring the various error sources in accelerators and their impact on the machine's performances. The book then demonstrates the latest developments of beam-based correction techniques that can be used to address such errors and covers the new and expanding area of beam-based optimization. This book is an ideal, accessible reference book for physicists working on accelerator design and operation, and for postgraduate studying accelerator physics. Features: Entirely self-contained, exploring the theoretic background, including algorithm descriptions, and providing application guidance Accompanied by source codes of the main algorithms and sample codes online Uses real-life accelerator problems to illustrate principles, enabling readers to apply techniques to their own problems Xiaobiao Huang is an accelerator physicist at the SLAC National Accelerator Laboratory at Stanford University, USA. He graduated from Tsinghua University with a Bachelor of Science in Physics and a Bachelor of Engineering in Computer Science in 1999. He earned a PhD in Accelerator Physics from Indiana University, Bloomington, Indiana, USA, in 2005. He spent three years on thesis research work at Fermi National Accelerator Laboratory from 2003-2005. He has worked at SLAC as a staff scientist since 2006. He became Accelerator Physics Group Leader of the SPEAR3 Division, Accelerator Directorate in 2015. His research work in accelerator physics ranges from beam dynamics, accelerator design, and accelerator modelling and simulation to beam based measurements, accelerator control, and accelerator optimization. He has taught several courses at US Particle Accelerator School (USPAS), including Beam Based Diagnostics, Accelerator Physics, Advanced Accelerator Physics, and Special Topics in Accelerator Physics.
Intelligent Beam Control in Accelerators
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.