Alignment of three mirror anastigmat telescopes using a multilayered stochastic parallel gradient descent algorithm

Solvay Blomquist, Heejoo Choi, Hyukmo Kang, Kevin Derby, Pierre Nicolas, Ewan S. Douglas, Daewook Kim
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Abstract

When a telescope doesn't reach a reasonable point spread function on the detector or detectable wavefront quality after initial assembly, a coarse phase alignment on-sky is crucial. Before utilizing a closed loop adaptive optics system, the observatory needs a strategy to actively align the telescope sufficiently for fine wavefront sensing. This paper presents a method of early-stage alignment using a stochastic parallel-gradient-descent (SPGD) algorithm which performs random perturbations to the optics of a three mirror anastigmat telescope design. The SPGD algorithm will drive the telescope until the wavefront error is below the acceptable range of the fine adaptive optics system to hand the telescope over. The focused spot size over the field of view is adopted as a feed parameter to the SPGD algorithm and wavefront peak-to-valley error values are monitored to directly compare our mechanical capabilities to our alignment goal of diffraction limited imaging and fine wavefront sensing.
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使用多层随机并行梯度下降算法校准三面镜定焦距望远镜
当望远镜在初始组装后,探测器上的点扩散函数或可探测波面质量达不到合理水平时,在天空中进行粗相位校准至关重要。在使用闭环自适应光学系统之前,天文台需要一种策略来主动充分地校准望远镜,以实现精细波前传感。本文介绍了一种使用随机并行梯度下降算法(SPGD)进行早期对准的方法,该算法对三镜面星轴望远镜的光学系统进行随机扰动。SPGD 算法将驱动望远镜,直到波前误差低于精细自适应光学系统的可接受范围,才将望远镜交出。视场上的聚焦光斑大小被用作 SPGD 算法的馈送参数,波前峰谷误差值受到监控,以直接比较我们的机械能力与衍射限制成像和精细波前传感的校准目标。
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