Neural-network-based wavefront solution algorithm for a wide field survey telescope

Abstract

The wide field survey telescope (WFST) is a 2.5 m optical survey telescope currently under construction in China. The telescope employs a primary-focus optical design to achieve a wide field of view of 3 deg, and its focal plane is equipped with four pairs of curvature sensors to perform wavefront sensing and active optics. Currently, there are several wavefront solution algorithms available for curvature sensors, including the iterative fast Fourier transform method, orthogonal series expansion method, Green’s function method, and sensitivity matrix method. However, each of these methods has limitations in practical use. This study proposes a solution method based on a convolutional neural network model with a U-Net structure for the curvature wavefront sensing of the WFST. Numerical simulations show that the model, when properly trained, has a high accuracy and performs a curvature wavefront solution effectively. Upon a comparison with the sensitivity matrix method, this new method demonstrates its superiority. Finally, the study is summarized, and the drawbacks of the proposed method are discussed, which leads to direction for future optimizations.