Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

Understand

We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning.

• We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment, and using this surrogate model in turn to train the neural network for its regulation task.
• We additionally show how the neural networks to be deployed for control purposes may be compiled to execute on field-programmable gate arrays.
• This capability is important for operational stability in complicated environments such as an accelerator facility.