标题： 内核相位和冠状仪术与自动微分 显示英文标题

标题： Kernel Phase and Coronagraphy with Automatic Differentiation

摘要： 在望远镜的光路中像差的积累会导致图像中的失真和斑点，限制了高角分辨率相机的性能。 实现对微弱源（如行星）的最高灵敏度，使用硬件和数据分析软件都很重要。 虽然解析方法效率高，但实际系统更适合用数值方法建模，但具有许多参数的模型可能难以理解、优化和应用。 机器学习中开发的自动微分软件现在使得对任意光学系统相对于像差的导数计算变得简单。 我们将这一强大的新工具应用于增强高角分辨率天文学成像。 自校准可观测量，如“闭合相位”或“双谱”，在光学和射电天文学中被广泛用于减轻光学像差并实现高保真图像。 核相位是小相位误差极限下闭合相位的推广。 利用自动微分，我们在该框架内重现了现有的核相位理论，并展示了对Lyot日冕仪的扩展，发现了对抗相位噪声具有抗性的斑点自校准组合，但仅在非常高的波前质量条件下。 作为示例，我们重新分析了帕洛马自适应光学对alpha Ophiuchi双星的观测，发现新流程与现有标准一致。 我们介绍了一个新的Python包“morphine”，结合了这些思想，其接口类似于流行的poppy包，用于带有自动微分的光学模拟。 这些方法可能有助于通过梯度下降设计改进的天文光学系统。 显示英文摘要

摘要： The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivity to faint sources such as planets, using both hardware and data analysis software. While analytic methods are efficient, real systems are better-modelled numerically, but such models with many parameters can be hard to understand, optimize and apply. Automatic differentiation software developed for machine learning now makes calculating derivatives with respect to aberrations straightforward for arbitrary optical systems. We apply this powerful new tool to enhance high-angular-resolution astronomical imaging. Self-calibrating observables such as the 'closure phase' or 'bispectrum' have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the Lyot coronagraph, finding self-calibrating combinations of speckles which are resistant to phase noise, but only in the very high-wavefront-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary alpha Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package 'morphine' that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.