基于泽尼克多项式的曲率波前传感（CWFS）的HCIPy模拟
Simulating Zernike-based CWFSing with HCIPy
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中文总结 AI 辅助
本研究利用HCIPy模拟泽尼克模式传播，明确CWFS的最优传感距离，提出分阶设置传感平面的方案，并建议开发基于模式灵敏度的波前重构算法。
中文摘要 AI 辅助
本研究探讨基于泽尼克多项式的曲率波前传感（CWFS）的最优传感距离。利用开源Python包HCIPy，我们模拟了前100阶泽尼克模式在1至20000米不同传播距离下的情况，以探究波前传播与其空间复杂度之间的关系，从而理解理想化CWFS对各阶模式的灵敏度。研究发现，低空间阶模式存在不同的最优传感距离，而高空间阶模式则在指数级更短的最优传感距离下表现出分组行为。结果表明，当前CWFS存在灵敏度缺口，可通过为低空间阶区域采用大量传感平面（每个低阶模式对应一个传感平面）来解决；针对高阶区域，我们提出多个传感平面，每个平面对应以给定传播距离下最高模式灵敏度为中心的局部模式小组。此外，我们还讨论了处理高阶泽尼克模式的注意事项，以及需要在傅里叶空间开展进一步传播分析以扩展本研究的初步工作。最后，我们建议开发新的重构算法，该算法可根据单个传感距离下对各模式的灵敏度（增益）来权衡完整波前的重构能力。
英文摘要
This work explores optimal sensing distances in Zernike-based Curvature WaveFront Sensing (CWFS). By using HCIPy, an open-source Python package, we simulate the first 100 Zernike modes at a variety of propagation distances (1 to 20,000 m) to explore the relationship between the propagation of a wavefront and its spatial complexity to understand an idealized CWFS' sensitivity to individual modes. We find that there exist distinct optimal sensing distances for low-spatial-order-modes, with high-spatial-order modes demonstrating grouping behavior at exponentially shorter optimal sensing distances. Our results indicate that there is a sensitivity gap in current CWFSing that can be resolved by resorting to a high number of sensing planes for the low spatial order regime, with one sensing plane for each individual low-order mode; similarly, we propose several high-order regime sensing planes, with each sensing plane dedicated to small groups of localized modes centered around the highest modal sensitivity at a given propagation distance. We also discuss the caveats in dealing with high-order Zernike modes and the need for carrying out further propagation analysis in the Fourier space to extend preliminary work presented here. Lastly, we suggest the development of a new reconstruction algorithm that weighs the ability to reconstruct a complete wavefront according to the sensitivity (gain) to each mode at an individual sensing distance.
