Graph neural networks for particle accelerator facilities

Abstract

A method or tool for an efficient, data-driven approach to beam tuning in particle accelerators that leverages deep learning over structured data. A beamline is represented as a graph, where individual elements are nodes and relationships between elements are edges. Element parameters are captured as node features. A graph neural network is used to generate a whole-graph embedding that preserves structural information, and dimensionality reduction techniques are applied to visualize low-dimensional representations. Providing a category label for each embedding to identify optimal regions of parameter space. The method serves as a global diagnostic, inasmuch as it monitors a high-dimensional space and provides feedback to operators when changes are made. Operators can track if changes during beam tuning move the configuration away or towards optimal regions of parameter space. On-line and off-line tool embodiments of the beam tuning tool are described.