AUTHOR=Wang Song , Tennant Chris , Moser Daniel , Larrieu Theo , Li Jundong TITLE=Graph learning for particle accelerator operations JOURNAL=Frontiers in Big Data VOLUME=7 YEAR=2024 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1366469 DOI=10.3389/fdata.2024.1366469 ISSN=2624-909X ABSTRACT=

Particle accelerators play a crucial role in scientific research, enabling the study of fundamental physics and materials science, as well as having important medical applications. This study proposes a novel graph learning approach to classify operational beamline configurations as good or bad. By considering the relationships among beamline elements, we transform data from components into a heterogeneous graph. We propose to learn from historical, unlabeled data via our self-supervised training strategy along with fine-tuning on a smaller, labeled dataset. Additionally, we extract a low-dimensional representation from each configuration that can be visualized in two dimensions. Leveraging our ability for classification, we map out regions of the low-dimensional latent space characterized by good and bad configurations, which in turn can provide valuable feedback to operators. This research demonstrates a paradigm shift in how complex, many-dimensional data from beamlines can be analyzed and leveraged for accelerator operations.