论文标题
用于加速器控制的实时人工智能：Fermilab助推器的研究
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
论文作者
论文摘要
我们描述了一种使用通过增强学习训练的神经网络精确调节Fermilab增强加速器复合物的梯度磁铁电源的方法。我们通过在实际加速器数据上训练替代机器学习模型来模仿助推器环境，并使用此替代模型依次训练神经网络执行其调节任务，从而证明了初步结果。我们还展示了如何用于控制目的的神经网络如何在现场可编程的门数组中执行。该功能对于在复杂环境（例如加速器设施）中的操作稳定性很重要。
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.
