Opportunities in Machine Learning for Particle Accelerators

Abstract

Machine learning" (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators [1].

Research on tune feedback of the Hefei Light Source II based on machine learning

“…To meet the design specifications or further improve beam stability, appropriate measures must be proposed for the machine. Although existing technologies are being used to improve beam stability, new technologies are being sought to improve beam stability [1][2][3][4].…”

Submacropulse electron-beam dynamics correlated with higher-order modes in Tesla-type superconducting rf cavities

“…Machine Learning is undergoing a renaissance in a wide variety of applications due to larger computational resources, advanced theoretical models, and successful practical applications during the last years. Particle accelerators are part of this resurgence of ML due to developments of new system modelling techniques, virtual instrumentation/diagnostics, tuning and control schemes, surrogate models, among others [17]. In this section, we evaluate a neural network (NN) model for bunch-by-bunch centroid slewing prediction, and its application in a ML-based optimization and model construction for HOM signal level reduction and emittance preservation.…”

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

“…Recent years have seen a boost in the development of machine learning (ML) algorithms and their applications [1]. With the rapid growth of data volume and processing capabilities, the value of data has been increasingly recognized both academically and in social life, and the prospect of ML has been pushed to an unprecedented level.…”

“…The highly complicated operation conditions and precise control objectives fall perfectly inside ML's scope [2]. Over the past few years, the interest and engagement of ML applications in the field of particle accelerators has started to grow [1,3] along with the trend of data-driven approaches in other disciplines. Fast and accurate ML-based beam dynamics modelling can serve as a guide for future accelerator design and commissioning [4].…”