Daniele Filippetto, a staff scientist in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division and deputy director of the Berkeley Accelerator Controls and Instrumentation Program (BACI) program, is featured in this article about a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Filippetto and colleagues developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Machine Learning Paves Way for Smarter Particle Accelerators
July 19, 2022 / Will Ferguson / Berkeley Lab News Center