Piston Error Automatic Correction for Segmented Mirrors via Deep Reinforcement Learning

Abstract

The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements compared with other methods. However, it is often difficult to obtain a high accuracy in practical application situations with this method because of the difference between the training model and the actual model. The reinforcement learning algorithm does not need to model the real system when operating the system. However, it still retains the advantages of supervised learning. Thus, in this paper, we placed a mask on the pupil plane of the segmented telescope optical system. Moreover, based on the wide spectrum, point spread function, and modulation transfer function of the optical system and deep reinforcement learning—without modeling the optical system—a large-range and high-precision piston error automatic co-phase method with multiple-submirror parallelization was proposed. Finally, we carried out relevant simulation experiments, and the results indicate that the method is effective.