The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static non-common-path aberrations (NCPA). Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging sequential phase diversity. We extend previous work in reinforcement learning for AO to focal plane control. A new model-based RL algorithm, Policy Optimization for NCPAs (PO4NCPA), interprets the focal-plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non-coronagraphic and post-coronagraphic PSFs without prior system knowledge. Further, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water-vapor-induced seeing (dynamic NCPAs). Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal-plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamics NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics. The method remains effective for the ELT pupil, vector vortex coronagraph, and under photon and background noise. PO4NCPA is model-free and can be directly applied to standard imaging as well as to any coronagraph. Its sub-millisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI.

翻译：直接成像潜在宜居系外行星是极大望远镜高对比度成像仪器的主要科学目标之一。大多数此类系外行星运行在宿主恒星附近，其观测受到快速移动的大气散斑和准静态非共路像差（NCPA）的限制。传统的NCPA校正方法常使用机械镜面探针，这会在运行过程中影响性能。本工作提出基于机器学习的NCPA控制方法，通过利用序列相位多样性自动检测并校正动态和静态NCPA误差。我们将强化学习在自适应光学中的先前工作扩展到焦平面控制。一种新的基于模型的强化学习算法——面向NCPA的策略优化（PO4NCPA），将焦平面图像作为输入数据，并通过序列相位多样性确定相位校正，从而在无需系统先验知识的情况下优化非日冕和日冕后的点扩散函数。进一步，我们通过数值模拟地面望远镜上受水汽视宁度影响的静态NCPA误差（动态NCPA），验证了该方法的有效性。模拟结果表明，PO4NCPA能够稳健地补偿静态和动态NCPA。在静态情况下，使用日冕仪时它实现了近最优的焦平面光抑制，无日冕仪时则达到近最优的斯特列尔比。在动态NCPA情况下，该方法在这些指标上与结合一步延迟积分器的模态最小二乘重构性能相当。该方法对ELT光瞳、矢量涡旋日冕仪以及在光子噪声和背景噪声下均保持有效。PO4NCPA是无模型的，可直接应用于标准成像及任意日冕仪。其亚毫秒级的推理时间和性能也使其适用于超越HCI的大气湍流实时低阶校正。