Foreword
It is an honor and a pleasure to write the foreword to this overview of beambased methods for improving the performance of particle accelerators.
The timing of this book is excellent. The new generation of particle accelerators now proposed or in construction have taken full advantage of the latest
improvements in accelerator design and engineering, including magnet construction, survey and alignment, vacuum technology, RF, beam diagnostics,
and lattice design, both linear and nonlinear. The result is accelerator performance pushed well beyond that which would have been reasonable to pursue
only a decade ago. Advanced techniques in beam-based accelerator control will
be required to commission and realize the full potential of these advanced new
machines. This book gives a review of experimental techniques used to find and
correct errors and maximize accelerator performance. These techniques range
from well-known, basic measurements to the latest innovations in beam-based
optimization and machine learning. The focus is on algorithms that efficiently
get results and have been proven to make measurable improvements to the
performance of existing accelerators. This book provides an essential guide for
the physicists who commission and operate these accelerators.
Dr. Xiaobiao Huang is well-suited to present this material. I have had the
good fortune to work with Dr. Huang for the past 13 years, since he came to
the Stanford Linear Accelerator Center to work at SSRL early in his career
as an accelerator physicist. He had already established a name for himself
in developing beam-based accelerator control techniques in his previous work
as a graduate student with S.Y. Lee at Indiana University and as a research
associate at Fermilab, in particular for his work using independent component analysis (ICA) to debug accelerator linear optics. Since then, he has
made many additional important contributions, including improving the orbit response matrix analysis (LOCO) so it produces more stable and reliable
results for fitted accelerator linear optics; developing a number of beam-based
optimization algorithms, including robust conjugate direction search (RCDS),
that have succeeded in improving nonlinear optics; and, most recently, applying machine learning techniques for online accelerator optimization. Dr.
Huang’s work brings the full power of the latest computational algorithms
to accelerator optimization. His combined expertise in experimental measurement, mathematical analysis, and computer programming have naturally led
to his playing a central role in this rapidly developing field.
James Safranek
ix
It is an honor and a pleasure to write the foreword to this overview of beambased methods for improving the performance of particle accelerators.
The timing of this book is excellent. The new generation of particle accelerators now proposed or in construction have taken full advantage of the latest
improvements in accelerator design and engineering, including magnet construction, survey and alignment, vacuum technology, RF, beam diagnostics,
and lattice design, both linear and nonlinear. The result is accelerator performance pushed well beyond that which would have been reasonable to pursue
only a decade ago. Advanced techniques in beam-based accelerator control will
be required to commission and realize the full potential of these advanced new
machines. This book gives a review of experimental techniques used to find and
correct errors and maximize accelerator performance. These techniques range
from well-known, basic measurements to the latest innovations in beam-based
optimization and machine learning. The focus is on algorithms that efficiently
get results and have been proven to make measurable improvements to the
performance of existing accelerators. This book provides an essential guide for
the physicists who commission and operate these accelerators.
Dr. Xiaobiao Huang is well-suited to present this material. I have had the
good fortune to work with Dr. Huang for the past 13 years, since he came to
the Stanford Linear Accelerator Center to work at SSRL early in his career
as an accelerator physicist. He had already established a name for himself
in developing beam-based accelerator control techniques in his previous work
as a graduate student with S.Y. Lee at Indiana University and as a research
associate at Fermilab, in particular for his work using independent component analysis (ICA) to debug accelerator linear optics. Since then, he has
made many additional important contributions, including improving the orbit response matrix analysis (LOCO) so it produces more stable and reliable
results for fitted accelerator linear optics; developing a number of beam-based
optimization algorithms, including robust conjugate direction search (RCDS),
that have succeeded in improving nonlinear optics; and, most recently, applying machine learning techniques for online accelerator optimization. Dr.
Huang’s work brings the full power of the latest computational algorithms
to accelerator optimization. His combined expertise in experimental measurement, mathematical analysis, and computer programming have naturally led
to his playing a central role in this rapidly developing field.
James Safranek
ix