AI 中文总结
本研究利用'imaka GLAO演示器'的开环遥测数据，通过多波前传感器的时空互相关识别湍流层速度，验证了风廓线方法的有效性，为AO系统开发提供了大气数据支撑。
AI 中文摘要
地面天文台使用的自适应光学（AO）系统，可通过更深入地理解其需校正的大气特性，在设计和算法两方面得到强化。这一点在莫纳克亚山（Maunakea）体现得尤为明显，该地区更清晰的大气轮廓信息，可用于指导从极端自适应光学（ExAO）的小间距到地面层自适应光学（GLAO）的宽视场等不同AO系统的开发。我们利用夏威夷大学2.2米望远镜上的'imaka GLAO演示器'获取的遥测数据，采用一种风廓线方法，通过多个波前传感器（WFS）的时空互相关来识别湍流层速度。我们将推导得到的层速度与附近风速计数据及气象模型对高层风速的预测结果进行对比，探讨两者的异同。通过在真实遥测数据中成功恢复注入的模拟层，我们评估了该廓线方法的优势与局限性。我们详细呈现了该廓线仪的结果，包括冬季和夏季目标观测期间，四次'imaka'特征观测运行中具备有效估计的数据百分比。我们报告了这些层与外部测量结果的相似程度、结果的置信度，以及该技术未来在其他多共轭自适应光学系统中的应用潜力。
英文摘要
Adaptive Optics (AO) used in ground based observatories can be strengthened in both design and algorithms by a more detailed understanding of the atmosphere they seek to correct. Nowhere is this more true than on Maunakea, where a clearer profile of the atmosphere informs AO system development from the small separations of Extreme AO (ExAO) to the wide field Ground Layer AO (GLAO). Employing telemetry obtained from the 'imaka GLAO demonstrator on the University of Hawaii 2.2-meter telescope, we apply a wind profiling method that identifies turbulent layer velocities through spatial-temporal cross correlations of multiple wavefront sensors (WFSs). We compare the derived layer velocities with nearby wind anemometer data and meteorological model predictions of the upper wind speeds and discuss similarities and differences. The strengths and limitations of this profiling method are evaluated through successful recovery of injected, simulated layers into real telemetry. We detail the profilers' results, including the percentage of data with viable estimates, on four characteristic 'imaka observing runs on open loop telemetry throughout both winter and summer targets. We report on how similar layers are to external measures, the confidence of these results, and the potential for future use of this technique on other multi conjugate AO systems.
