Speaker
Description
The Advanced Photon Source (APS) facility has just completed an upgrade to become one of the world’s brightest storage-ring light sources. For the first time, machine learning (ML) methods have been extensive used as part of the baseline commissioning plan. Most popular such method was Bayesian optimization (BO) – a tool for efficient online high-dimensional single and multi-objective tuning. In this paper we will present our BO development work on experimentally motivated augmentations - uncertainty-aware simulation priors, parameter space and acquisition function refinement for multi-objective optimization, and online execution time improvements. These improvements were integrated into the APSopt optimizer, which was then successfully used for various commissioning tasks. We will show results of tuning linac and booster transmission efficiency, injection trajectory stabilization, and of extensive multi-objective storage ring dynamic/momentum aperture studies. Given the success of BO methods, work is proceeding on tighter integration into the control room.
