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

Abstract

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.