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

TL;DR

This paper presents an adaptive optics system utilizing stochastic parallel gradient descent (SPGD) to enhance high-contrast imaging in coronagraphs by reducing speckle noise, demonstrated with experimental results showing significant contrast improvement.

Contribution

It introduces a novel SPGD-based adaptive optics approach for coronagraphs, demonstrating its effectiveness with a deformable mirror and liquid crystal array in experimental setups.

Findings

Contrast improved from 10^-3 to 10^-4.5 at 2λ/D

Effective speckle noise reduction in coronagraphic imaging

Feasibility demonstrated with experimental setup

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.