Visible pyramid wavefront sensing approach for daylight adaptive optics

Abstract

Daytime application of the pyramid wavefront sensor (PyWFS) is greatly challenged by a bright and fluctuating sky background, especially in the visible. A daytime-Py approach to apply visible pyramid wavefront sensing for real-time daylight AO is described in this paper. A field stop (FS) and a lenslet array are applied in the daylight AO system based on a visible PyWFS to separate the object signal from the background signal and improve the signal-to-noise ratio (SNR). A background elimination algorithm is proposed to extract the effective object signal. Closed-loop experiment using the daytime-Py approach is performed, which presents the first laboratory real-time daylight natural guide star AO correction of a faint object based on a visible PyWFS. SNR ranges for both the daytime-Py approach and PyWFS are reported. Furthermore, the correction results in different SNRs using both methods and with various pupil samplings using the daytime-Py approach are presented to prove that our proposal has the advantages over the PyWFS and Shack-Hartmann wavefront sensor (SHWFS) for daylight AO. This study demonstrates that the daytime-Py approach can realize the real-time object tracking and closed-loop correction in the daylight natural guide star adaptive optics (AO) system based on the visible PyWFS.