Vol 11, No 8

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

Bing Dong, De-Qing Ren, Xi Zhang

Abstract

Abstract 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 briefly 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 contrast shows improvement from 10−3 to 10−4.5 at an angular distance of 2λ/D after being corrected by SPGD based AO. 

Keywords

Keywords instrumentation: adaptive optics — methods: laboratory — techniques: image processing, coronagraph

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