Stochastic parallel gradient descent based adaptive optics used for high contrast imaging coronagraph

Abstract: An adaptive optics (AO) system based on stochastic parallel gradient descent (SPGD) algorithm is proposed to reduce the speckle noises in the optical system of stellar coronagraph in order to further improve the contrast. The principle of SPGD algorithm is described briefly and a metric suitable for point source imaging optimization is given. The feasibility and good performance of SPGD algorithm is demonstrated by experimental system featured with a 140-actuators deformable mirror (DM) and a Hartmann- Shark wavefront sensor. Then the SPGD based AO is applied to a liquid crystal array (LCA) based coronagraph. The LCA can modulate the incoming light to generate a pupil apodization mask in any pattern. A circular stepped pattern is used in our preliminary experiment and the image contrast shows improvement from 10^-3 to 10^-4.5 at angular distance of 2{\lambda}/D after corrected by SPGD based AO.