AI-Assisted Transport of Radioactive Ion Beams

An AI-assisted system optimizes transport of radioactive ion beams, enhancing efficiency and scientific output. This method, tested at Argonne National Laboratory, outperforms traditional tuning techniques, with potential global application to improve operational efficiency and nuclear research. AI, including Machine Learning and Bayesian Optimization, automates beam tuning, using Gaussian Processes for optimization. The approach addresses challenges through unknown or costly variable relations, with applications in various fields like laser wakefield accelerators, linear optics correction, and beam alignment in storage rings.