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

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