AI 中文总结
该研究训练卷积神经网络作为非线性相位重构器，将矢量泽尼克波前传感器的动态范围大幅扩展，成功实现其闭环观测，性能优于线性重构器，拓展了自适应光学系统的应用场景。
AI 中文摘要
背景：新一代巨型拼接镜望远镜将采用自适应光学以达到分辨率的基本极限。为此，研究人员设计了新型波前传感器（WFS）以满足需求，但鉴于这类波前传感器的信号特性，其运行可能需要非线性重构技术。目标：本文表明，可利用非线性重构器将最灵敏的波前传感器之一——泽尼克波前传感器（ZWFS）的动态范围大幅扩展至远超设计极限。方法：我们完全在模拟环境中训练卷积神经网络（CNN），用于对矢量泽尼克波前传感器（v-ZWFS，ZWFS的高动态范围变体）进行相位重构。与线性方法不同，CNN利用全帧信息重构各点相位，可解决ZWFS周期性响应引入的模糊性，从而扩展其有效捕获范围。我们开发了两步训练策略以确保闭环稳定性，并采用物理信息损失函数最大化CNN性能。结果：我们在观测条件下成功利用CNN实现了v-ZWFS的闭环观测，而线性重构器在此类条件下无法收敛至稳定的平面波前。在两种重构方法均有效的场景中，CNN在几乎所有情况下均优于线性方法；在良好的视宁度条件下，我们甚至能以ZWFS作为第一级WFS实现闭环，凸显了非线性波前重构器带来的扩展动态范围。结论：我们得出结论，非线性波前重构器的使用可扩展自适应光学系统的应用场景，尤其当所用WFS表现出高度非线性行为时。
英文摘要
Context: The new giant segmented mirror telescopes will use adaptive optics to reach the fundamental limits in resolving power. To accomplish this, new wavefront sensors (WFS) have been designed to fulfill the requirements, but they may require non-linear reconstruction techniques to operate given the nature of the signal of the WFSs Aims: In this article we show that it is possible to use non-linear reconstructors to extend the dynamic range of one of the most sensitive wavefront sensors far beyond the designed limits: the Zernike wavefront sensor (ZWFS). Methods: We trained a convolutional neural network (CNN) completely in simulation to perform the phase reconstruction of a vector-ZWFS (v-ZWFS), a higher dynamic-range variant of the ZWFS. Contrary to the linear method, the CNN uses the information across the full frame to reconstruct the phase at each point, enabling it to resolve the ambiguities introduced by the periodic response of the ZWFS and thereby extend its effective capture range. We developed a two-step training strategy that ensured closed-loop stability and used a physically informed loss function to maximize the performance of the CNN. Results: We successfully closed the loop on-sky with the v-ZWFS using the CNN in observing conditions that the linear reconstructor could not converge to a stable flat wavefront. In cases where both reconstruction methods were working, the CNN outperformed the linear method in almost all cases, and in favorable seeing conditions we were even able to close the loop with the ZWFS acting as a first stage WFS, highlighting the extended dynamic range brought by the use of a non-linear wavefront reconstructor. Conclusions: We conclude that the use of non-linear wavefront reconstructor can extend the use cases of adaptive optics systems, especially when the WFS used shows highly non-linear behaviors.
