Modern accelerators are among the most complicated and delicate computer-controlled systems. An accelerator such as the Linac Coherent Light Source (LCLS) can consist of tens of thousands of components, each of which needs to be thoroughly controlled and monitored in order for the machine to properly work. Realizing the maximum machine performance and maintaining high availability for such a machine pose a tremendous challenge.
Machine learning (ML) may hold the key to addressing this challenge. As a broad field of study, ML offers algorithms and methods for modeling, optimizing, and automatic controlling of systems of all scales, which could be ideal for many accelerator applications.
Tuning SPEAR3 dynamic aperture with the RCDS method. X. Huang, J. Safranek, Phys. Rev. ST Accel. Beams, 18, 084001 (2015)
Prediction of longitudinal phase space with a Neural Network based surrogate model. C. Emma, A. Edelen, et al, PRAB 21,112802 (2018)
Illustration of the ghost imaging experimental setup. Figure author: Thomas J. Lane. Figure under CC4.0 license.