Machine learning applications in large particle accelerator facilities: review and prospects

Abstract

Rapid growth of machine learning techniques has arisen over last decades, which results in wide applications of machine learning for solving various complex problems in science and engineering. In the last decade, machine learning and big data techniques have been widely applied to the domain of particle accelerators and a growing number of results have been reported. Several particle accelerator laboratories around the world have been starting to explore the potential of machine learning the processing the massive data of accelerators and to tried to solve complex practical problems in accelerators with the aids of machine learning. Nevertheless, current exploration of machine learning application in accelerators is still in a preliminary stage. The effectiveness and limitations of different machine learning algorithms in solving different accelerator problems have not been thoroughly investigated, which limits the further applications of machine learning in actual accelerators. Therefore, it is necessary to review and summarize the developments of machine learning so far in the accelerator field. This paper mainly reviews the successful applications of machine learning in large accelerator facilities, covering the research areas of accelerator technology, beam physics, and accelerator performance optimization, and discusses the future developments and possible applications of machine learning in the accelerator field.