论文标题
在巨型分段镜望远镜上的多连接式自适应光学元件的方向依赖点扩展功能重建
Direction dependent Point spread function reconstruction for Multi-Conjugate Adaptive Optics on Giant Segmented Mirror Telescopes
论文作者
论文摘要
现代巨型分割的镜像望远镜（GSMT），例如目前正在施工的极大望远镜（ELT），在很大程度上取决于自适应光学（AO）系统，以校正大气湍流。为了能够纠正更广泛的视野（FOV），引入了多连接的自适应光学（MCAO）系统，使用多个导向恒星来获得对FOV的几乎均匀校正。然而，由于波前传感器（WFS）的积分时间和可变形镜（s）（dm）的时间响应的时间延迟，因此在天体图像中保留了残留的模糊。这会导致模糊，该模糊可以通过与点扩散函数（PSF）的真实图像的卷积来数学描述。由于大气的性质及其校正，PSF在空间上有所不同。 在本文中，我们提出了一种MCAO PSF重建算法，该算法以存储有效的方式适应GSMT的需求。特别是，[40]的单个共轭自适应光学元件（SCAO）的PSF重建算法与[33]的大气层层析成像算法结合使用，以获得后AO PSF的方向重建。 与直接从模拟传入波前计算的PSF相比，在端到端仿真工具中获得的结果在定性上呈良好的重建。此外，使用的算法具有合理的运行时和内存消耗。
Modern Giant Segmented Mirror Telescopes (GSMT) like the Extremely Large Telescope (ELT), currently under construction depend heavily on Adaptive Optics (AO) systems to correct for atmospheric turbulence. To be able to correct wider fields of view (FoV), Multi-Conjugate Adaptive Optics (MCAO) systems were introduced, which use multiple guide stars to obtain an almost uniform correction over the FoV. However, a residual blur remains in the astronmical images due to the time delay stemming from the wavefront sensor (WFS) integration time and temporal response of the deformable mirror(s) (DM). This results in a blur which can be mathematically described by a convolution of the true image with the point spread function (PSF). Due to the nature of the atmosphere and its correction, the PSF is spatially varying. In this paper, we present an algorithm for MCAO PSF reconstruction adapted to the needs of GSMTs in a storage efficient way. In particular, the PSF reconstruction algorithm for Single Conjugate Adaptive Optics (SCAO) from [40] is combined with an algorithm for atmospheric tomography from [33] to obtain a direction dependent reconstruction of the post-AO PSF. Results obtained in an end-to-end simulation tool show qualitatively good reconstruction of the PSF compared to the PSF calculated directly from the simulated incoming wavefront. Furthermore, the used algorithm has a reasonable runtime and memory consumption.
