Speaker
Description
Developing a multifaceted ML tool for day-to-day operation of a particle accelerator requires close collaboration with the machine operators to incorporate their experience and feedback. The tool must also ensure that all safety protocols and operational redundancies established by the operations team are fully integrated. In the initial part of this talk we briefly present the framework of an ML tool currently in use and its supporting control system architecture. Details on the underlying Bayesian Optimization will be highlighted for tuning various sections of the accelerator along with recent implementation of the capability to use prior experiment data for any beamline of the ATLAS accelerator significantly improving the tuning procedure. This has been made possible by recent availability of complete ATLAS database including information of beam current, element settings, operator notes associated with every experiment going back to 2005. Improvements made to the tool based on extensive accelerator operator feedback during its use over the past year will be discussed. Requested features by the operators such as the capability for sequential simultaneous tuning of multiple beamlines will be discussed. With the recent interest in increasing operational beam current at ATLAS, various pain points of the tool in this high-risk operating condition are presented. The overall evolution of the tool from its initial PRO version to the currently implemented LITE version for rapid use by operators will be discussed.
