Work

Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning

Year: 2024

Type: article

Abstract: The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems.

Source: Photonics

DOI: 10.3390/photonics11111064

Authors: Kunkun Cheng, Shengqian Wang, Xuesheng Liu, Yuandong Cheng

Institutions: Chinese Academy of Sciences, Institute of Optics and Electronics, Chinese Academy of Sciences, University of Chinese Academy of Sciences

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, West Light Foundation of the Chinese Academy of Sciences