AI 中文总结
该研究实现了CNN重构的uPWFS，在MagAO-X上验证了其在轨校正效果，为未来极大望远镜的高对比度仪器提供了可行技术。
AI 中文摘要
地基极端自适应光学系统（XAO）的主要限制之一是时间噪声与光子噪声误差之间的平衡。未调制金字塔波前传感器（uPWFS）相比调制型传感器有望大幅提升灵敏度，但其实际应用受限于线性范围。非线性重构器为恢复该动态范围提供了途径，同时可保留uPWFS的灵敏度，从而降低光子噪声并提升对比度。本文展示了卷积神经网络（CNN）重构器的实时实现，并呈现了在MagAO-X上的在轨结果，证明其在不同大气条件下均能实现稳健稳定的校正。在低、中斯特列尔比（Strehl regime）条件下，该系统相比默认操作实现了显著提升，而在高斯特列尔比条件下性能略有下降。我们通过模拟对此进行分析，主要将其归因于高斯特列尔比条件下未优化的训练数据集，而非该方法的固有局限。此外，针对缩小版极大望远镜（ELT）开展的神经网络增强型uPWFS初始模拟显示，其在快速瓣活塞控制方面实现了大幅提升。这些结果表明，神经网络增强型波前传感是未来高对比度仪器的可行技术。
英文摘要
One of the main limitations of ground-based extreme adaptive optics systems (XAO) is the balance between the temporal and photon noise error. The unmodulated Pyramid Wavefront Sensor (uPWFS) promises significant gains in sensitivity over its modulated counterpart, but its practical use is limited by its linearity range. Nonlinear reconstructors provide a pathway to recover this dynamic range while preserving the sensitivity of the uPWFS, thereby reducing photon noise and improving contrast. We present the real-time implementation of a Convolutional Neural Network (CNN) reconstructor and show on-sky results with MagAO-X, demonstrating robust and stable correction across diverse atmospheric conditions. Significant gains over default operation are seen in the low and moderate Strehl regimes, while the performance is slightly degraded in the high Strehl regime. We diagnose this in simulation and mainly attribute this to a non-optimized training dataset for the high-Strehl regime, rather than a fundamental limitation of the approach. Furthermore, initial simulations of the NN-enhanced uPWFS for a downscaled version of the Extremely Large Telescope (ELT) show substantial gains for fast petal-piston control. These results demonstrate that NN-enhanced wavefront sensing is a viable technology for future high-contrast instruments.
Comments14 pages, 12 figures, SPIE Astronomical Telescopes + Instrumentation 2026
