Adaptive optics: Neural network wavefront sensing, reconstruction, and prediction

We introduce adaptive optics as a technique to improve images taken by ground-based telescopes through a turbulent blurring atmosphere. Adaptive optics rapidly senses the wavefront distortion referenced to either a natural or laser guidestar, and then applies an equal but opposite profile to an adaptive mirror. In this paper, we summarize the application of neural networks in adaptive optics. First, we report previous work on employing multi-layer perceptron neural networks and back-propagation to learn how to sense and reconstruct the wavefront. Second, we show how neural networks can be used to predict the wavefront, and compare the neural networks’ predictive power in the presence of noise to that of linear networks also trained with back-propagation. In our simulations, we find that the linear network predictors train faster, they have lower residual phase variance, and they are much more tolerant to noise than the non-linear neural network predictors, though both offer improvement over no prediction. We conclude with comments on how neural networks may evolve over the next few years as adaptive optics becomes a more routine tool on the new large astronomical telescopes.