Beam Profile Prediction of High-Repetition-Rate SBS Pulse Compression Using Convolutional Neural Networks

Fast prediction of beam quality in SBS pulse compression for high-repetition-rate operation is urgently important for SBS experimental parameter acquisition. In this study, a fast computational prediction model for SBS beam profiles is developed using a convolutional neural network (CNN) method, which is trained and validated using experimental data from SBS pulse compression experiments. The CNN method can predict beam spot images for experimental conditions in the range of 100–500 Hz repetition rates and 5–40 mJ injection energy. The proposed CNN-based SBS beam profile prediction model has a fast convergence of the loss function and an average error of 15% with respect to the experimental results, indicating a high accuracy of the model. The CNN-based prediction model achieves an average error of 11.8% for beam profile prediction across various experimental conditions, demonstrating its potential for SBS beam profile characterization. The CNN method could provide a fast means for predicting the characteristic law of the beam intensity distribution in high-repetition-rate SBS pulse compression systems.