A Browser Based Toolkit for Improved Particle Accelerator Controls

Modern particle accelerator facilities generate large amounts of data and face increasing demands on their operational performance. These datasets can be difficult to visualize and contain complex relationships that are challenging to understand. Additionally, as the demand on accelerator operations increases so does the need for automated tuning algorithms and control to maximize uptime with reduced operator intervention. Existing tools are insufficient to meet the broad demands on controls, visualization, and analysis. Our approach is to develop a web-based toolkit with access to advanced control and data analytics libraries in addition to particle accelerator simulations. This toolbox will be able to connect to the control system for data collection and deployment of control algorithms. We will equip the toolkit with community supported tracking codes, thereby enabling users to simulate machine dynamics and develop reduced models for low level control systems and machine drift. During Phase I we will build a prototype graphical web interface for data analysis using Python libraries; we will stand up a lightweight version of the EPICS control system with parameters that model the AGS to RHIC transfer line at Brookhaven National Lab; and we will develop advanced control algorithms to improve the matching of the transfer line at different energies. At the conclusion of Phase I we will have proof of principle simulations of the transfer line at arbitrary beam energy using our controls toolkit. This toolkit will have broad applicability to both large and small accelerator facilities. The toolkit will connect realistic machine models to advanced control and analysis libraries. This linkage will greatly improve accelerator operations and free operations staff to focus on improving machine performance.