Piston detection in segmented telescopes via multiple neural networks coordination of feature-enhanced images

• Published in: Optics communications 2022-03, Vol.507, p.127617, Article 127617
• Main Authors: Zhao, Weirui, Wang, Hao, Zhang, Lu, Gu, Yun, Zhao, Yuejin
• Format: Article
• Language: English
• Subjects: Neural networks; Piston detection; Segmented telescope
• ISSN: 0030-4018; 1873-0310
• DOI: 10.1016/j.optcom.2021.127617

High-precision piston detection within a large capture range is a key for segmented telescopes. In this paper, we propose a simple and efficient piston detection method based on multiple neural networks coordination. By setting a mask with a sparse multi-subpupil configuration at conjugate plane of the segmented mirror, a new dataset that is extremely sensitive to the piston is created. And two kinds of neural networks are built for different stages of detection, which ensures the method is of both large-scale and high-precision. Simulation shows that the piston can be detected in the range of the coherence length of the operating light with a sub-nanometer scale precision by this method. This method is robust and does not require complex hardware. It can be widely applied in segmented and deployable primary mirror telescopes. •A simple and efficient piston detection method for segments based on multiple neural networks coordination is proposed.•A novel feature-enhanced PSF dataset is constructed to improve networks detection performance.•This detection method can achieve a large-scope and high-precision of piston detection simultaneously.•The precision of piston detection is not affected by the range at all.