Particle accelerators are extremely complex machines that are challenging to simulate,
design, and control. Over the past decade, artificial intelligence (AI) and machine learning …

Measurement and correction of charged particle beam optics have been a major concern
since the advent of strong focusing synchrotron accelerators. Traditionally, particle colliders …

This advanced textbook and reference is the first comprehensive and systematic review of
all methods used for the measurement, correction, and control of the beam dynamics of …

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and
outputs of large complex systems directly from data. However, for time-varying systems, the …

Optics measurement algorithms have been improved in preparation for the commissioning
of the LHC at higher energy, ie, with an increased damage potential. Due to machine …

Proton beams were successfully steered through the entire ring of the CERN Large Hadron
Collider (LHC) on September the 10th of 2008. A reasonable lifetime was achieved for the …

This book provides systematic coverage of the beam-based techniques that accelerator
physicists use to improve the performance of large particle accelerators, including …

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a
critical factor in improving regular operations and future runs. Robust identification of …

Autocorrelation is applied to analyze sets of finite-sampling data such as the turn-by-turn
beam position monitor (BPM) data in an accelerator. This method of data analysis, called the …

Bunch-by-bunch beam parameters monitor is a very useful tool for accelerator operation and
optimization. An in situ bunch-by-bunch three-dimensional position and charge …