发表机构
STAR Institute, Université de Liège; European Southern Observatory（列日大学 STAR 研究所; 欧洲南方天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对地面望远镜高对比度成像的非共光路像差问题，采用强化学习与焦平面波前传感，测试PO4NCPA算法在不同涡日冕仪上的性能，验证其灵活性。
AI 中文摘要
地面望远镜的高对比度成像（HCI）受大气湍流导致的观测波前像差影响。自适应光学（AO）系统擅长校正这些像差，但在校正非共光路像差（NCPAs）方面存在不足，NCPAs的产生源于波前传感器（WFS）测量并校正的波前与影响科学图像的波前不同，因此需要额外干预。本研究利用焦平面波前传感和强化学习（RL）解决NCPAs导致的波前像差问题。PO4NCPA算法采用序列相位多样性解决相位模糊，并在针对中红外极大望远镜成像光谱仪（METIS）设计的模拟环境中测试。本文展示了PO4NCPA在标量和矢量涡日冕仪上的性能，以证明其灵活性。
英文摘要
High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This work makes use of focal-plane wavefront sensing and reinforcement learning (RL) to address the wavefront aberrations caused by NCPAs. The PO4NCPA algorithm utilizes sequential phase diversity to address phase ambiguities and is tested on a simulation designed for the Mid-infrared ELT Imager and Spectrograph (METIS) instrument. In this paper, we present the performance of PO4NCPA with scalar and vector vortex coronagraphs to demonstrate its flexibility.
Comments11 pages, 5 figures
Journal refIremsu Taskin, Jalo Nousiainen, Gilles Orban de Xivry, et al. "Exploring reinforcement learning to enhance focal plane wavefront correction for vortex coronagraphs", Proc. SPIE 14150, Adaptive Optics Systems X, 1415056 (20 Aug 2026)
