Beam Size Prediction and Control using Neural Network

Experimental results from beam lines are sensitive to the size of the beam itself. However, predicting and controlling the beam size still prove to be a challenge. Even with the feed-forward strategy using recorded control data (aka look-up tables), the beam size variance on vertical direction is still ~2 μm. Herein, we provide a machine learning based approach to predict the beam size using neural network. We perform a systematic study to optimize the prediction result using different neural network architectures and regularizers. Based on the model, we propose a neural network based beam size stablization strategy by tuning a certain experimental parameter (dispersion wave parameter). The variance of beam size on vertical direction is reduced to ~0.3 μm in the online test.