Modern accelerators are among the most complicated and delicate computer-controlled systems. An accelerator such as the Linac Coherent Light Source (LCLS) can consist of tens of thousands of components, each of which needs to be thoroughly controlled and monitored in order for the machine to properly work. Realizing the maximum machine performance and maintaing high availability for such a machine pose a tremendous challenge.
Machine learning (ML) may hold the key to addressing this challenge. As a broad field of study, ML offers algorithms and methods for modeling, optimizing, and automatic controlling of systems of all scales, which could be ideal for many accelerator applications.
At SLAC, we are developing ML methods for accelerators in the following areas:
- Automated tuning and control: Using advanced online optimization algorithms to efficiently search the parameter space, discover the optimal operation setting, and maintain the high performance.
- Beam diagnostics and data analysis: Analyzing beam diagnostics data with ML methods to provide accurate and detailed beam conditions and providing live beam information to users.
- Fault detection and prediction: Identifying root causes of machine faults by analyzing recent data of a vast number of process variables, predicting future machine faults based on models trained with archived history data.
- Accelerator modeling and simulation: Building comprehensive models for the accelerators with both physics simulation and deep learning, applying the models to accelerator diagnostics and control.