Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network

Abstract

To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.