Frazin Richard A, Rodack Alexander T
J Opt Soc Am A Opt Image Sci Vis. 2021 Oct 1;38(10):1557-1569. doi: 10.1364/JOSAA.426339.
The leading difficulty in achieving the contrast necessary to directly image exoplanets and associated structures (e.g., protoplanetary disks) at wavelengths ranging from the visible to the infrared is quasi-static speckles (QSSs). QSSs are hard to distinguish from planets at the necessary level of precision to achieve high contrast. QSSs are the result of hardware aberrations that are not compensated for by the adaptive optics (AO) system; these aberrations are called non-common path aberrations (NCPAs). In 2013, Frazin showed how simultaneous millisecond telemetry from the wavefront sensor (WFS) and a science camera behind a stellar coronagraph can be used as input into a regression scheme that simultaneously and self-consistently estimates NCPAs and the sought-after image of the planetary system (exoplanet image). When run in a closed-loop configuration, the WFS measures the corrected wavefront, called the AO residual (AOR) wavefront. The physical principle underlying the regression method is rather simple: when an image is formed at the science camera, the AOR modules both the speckles arising from NCPAs as well as the planetary image. Therefore, the AOR can be used as a probe to estimate NCPA and the exoplanet image via regression techniques. The regression approach is made more difficult by the fact that the AOR is not exactly known since it can be estimated only from the WFS telemetry. The simulations in the Part I paper provide results on the joint regression on NCPAs and the exoplanet image from three different methods, called ideal, naïve, and bias-corrected estimators. The ideal estimator is not physically realizable (it is useful as a benchmark for simulation studies), but the other two are. The ideal estimator uses true AOR values (available in simulation studies), but it treats the noise in focal plane images via standard linearized regression. Naïve regression uses the same regression equations as the ideal estimator, except that it substitutes the estimated values of the AOR for true AOR values in the regression formulas, which can result in problematic biases (however, Part I provides an example in which the naïve estimate makes a useful estimate of NCPAs). The bias-corrected estimator treats the errors in AOR estimates, but it requires the probability distribution that governs the errors in AOR estimates. This paper provides the regression equations for ideal, naïve, and bias-corrected estimators, as well as a supporting technical discussion.
在从可见光到红外波段直接成像系外行星及相关结构（如原行星盘）所需的对比度方面，主要困难在于准静态散斑（QSSs）。要达到高对比度，就需要在必要的精度水平上区分QSSs和行星，但这很难做到。QSSs是由硬件像差导致的，自适应光学（AO）系统无法对其进行补偿；这些像差被称为非共光路像差（NCPAs）。2013年，弗雷津展示了如何将来自波前传感器（WFS）和恒星日冕仪后面的科学相机的同步毫秒级遥测数据用作回归方案的输入，该方案能同时且自洽地估计NCPAs和所寻求的行星系统图像（系外行星图像）。当以闭环配置运行时，WFS测量校正后的波前，即AO残余（AOR）波前。回归方法背后的物理原理相当简单：当在科学相机上形成图像时，AOR对由NCPAs产生的散斑以及行星图像都进行了调制。因此，AOR可作为一个探针，通过回归技术来估计NCPA和系外行星图像。由于AOR只能从WFS遥测数据中估计出来，并不完全已知，这使得回归方法变得更加困难。第一篇论文中的模拟给出了三种不同方法对NCPAs和系外行星图像进行联合回归的结果，这三种方法分别称为理想估计器、朴素估计器和偏差校正估计器。理想估计器在物理上无法实现（它可作为模拟研究的基准），但另外两种是可以实现的。理想估计器使用真实的AOR值（模拟研究中可获得），但它通过标准线性化回归来处理焦平面图像中的噪声。朴素回归使用与理想估计器相同的回归方程，只是在回归公式中用AOR的估计值代替了真实的AOR值，这可能会导致有问题的偏差（然而，第一篇论文提供了一个例子，其中朴素估计对NCPAs做出了有用的估计）。偏差校正估计器处理AOR估计中的误差，但它需要控制AOR估计误差的概率分布。本文给出了理想估计器、朴素估计器和偏差校正估计器的回归方程，以及相关的技术支持讨论。
