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arXiv 1105.0976astro-ph.IM
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基于随机并行梯度下降的自适应光学用于冠状动脉高对比度成像

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

Bing Dong, Deqing Ren, Xi Zhang

摘要:

为了进一步提高日冕光学系统的对比度,提出了一种基于随机并行梯度下降(SPGD)算法的自适应光学(AO)系统。简要介绍了SPGD算法的原理,给出了一种适用于点源成像优化的度量。采用140个驱动器的变形镜和Hartmann-Shark波前传感器组成的实验系统验证了SPGD算法的可行性和良好性能。然后将基于SPGD的自适应光学应用于基于液晶阵列(LCA)的电晕放电。LCA可以调制入射光以生成任何图案的光瞳变迹掩模。在我们的初步实验中使用了圆形阶梯图案,并且在2λ/D的角距离处,图像对比度在基于SPGD的AO校正后从10^-3改善到10^-4.5。

英文摘要:

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 2λ/D after corrected by SPGD based AO.

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