发表机构
ONERA; Observatoire de Paris; CNRS（法国航空航天研究院; 巴黎天文台; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于深度强化学习的日冕仪后波前控制方法，利用焦平面图像和物理信息传感数据驱动变形镜，在模拟测试平台上成功生成暗洞，性能接近传统方法。
AI 中文摘要
系外行星的直接成像受到恒星与行星之间极端对比度的限制，这一问题可通过使用日冕仪来缓解。然而，光学像差会导致星光通过日冕仪时发生泄漏，产生掩盖行星信号的散斑。要达到所需的对比度水平，需要具有亚纳米精度的波前控制。深度强化学习为传统的焦平面波前控制技术提供了一种有前景的替代方案，它能够直接从与系统的交互中学习自适应校正策略。在本工作中，我们提出了一种完全数据驱动的方法，用于在模拟的高对比度成像测试平台中进行日冕仪后像差校正。智能体使用由焦平面测量（图像）和从这些图像中导出的基于物理信息的波前传感信息组成的观测值来控制变形镜。我们评估了不同的观测表示和控制策略，并在高对比度成像测试平台的简化模拟中验证了该方法，该方法成功创建了暗洞，即焦平面中残余星光被强烈抑制的区域，同时性能接近传统波前控制方法。
英文摘要
Direct imaging of exoplanets is limited by the extreme contrast between the star and the planets, which is mitigated using a coronagraph. However, optical aberrations cause starlight leakage through the coronagraph, producing speckles that obscure the planetary signal. Achieving the required contrast levels demands wavefront control with subnanometric precision. Deep reinforcement learning offers a promising alternative to traditional focal-plane wavefront control techniques by enabling adaptive correction strategies learned directly from interaction with the system. In this work, we present a fully data-driven method for post-coronagraphic aberration correction in a simulated high-contrast imaging testbed. The agent controls a deformable mirror using observations consisting of focal-plane measurements (images) and physics-informed wavefront sensing information derived from these images. We evaluate different observation representations and control strategies, and the method is validated on simplified simulations of a high-contrast imaging testbed, where it successfully creates dark holes, i.e., regions of the focal plane in which residual starlight is strongly suppressed, while approaching the performance of conventional wavefront control methods.
Journal refSPIE Astronomical Telescopes + Instrumentation, Jul 2026, Copenhague, Denmark. pp.98
