地平式太阳望远镜库德焦面指向跟踪误差建模研究

Study on Pointing and Tracking Model of Coude Focal Plane of Altazimuth Solar Telescope

为提高地平式太阳望远镜库德焦面的指向跟踪精度,首先仿真了库德光路中主光轴、方位轴、高度轴和消旋轴不同心时在焦面上引入的跟踪误差,分析了库德焦面指向跟踪误差的复杂性,这一问题无法用夜天文望远镜卡焦指向模型解决。然后提出了一种基于机器学习的支持向量回归方法构建太阳望远镜库德焦面的指向跟踪模型,并在1 m新真空太阳望远镜(New Vacuum Solar Telescope, NVST)进行了实测建模和实验验证。实验结果表明,在模型改正之前,1 m新真空太阳望远镜在库德焦面的指向误差最大值为650.55″、均方根(Root Mean Square, RMS)为115.88″,30 min跟踪误差为6.46″;模型改正之后,指向误差最大值为25.02″、均方根为3.98″,30 min跟踪误差为1.10″。由此可见,基于机器学习的支持向量回归建模方法能有效提高地平式太阳望远镜库德焦面的指向跟踪精度。

In order to improve the pointing and tracking accuracy of the Coude focal plane of the altazimuth solar telescope. Firstly, this paper simulates the tracking error introduced on the Coude focal plane when the main optical axis, azimuth axis, height axis and racemic axis of the folded optical axis of the horizon solar telescope are not concentric. The complexity of the pointing and tracking error of the Coude focal plane is analyzed, which can′t be solved by the usual night telescope focal pointing model. Then, the paper constructs pointing model based on support vector regression, and carries out an experiment on NVST. The experimental results show that before the model correction, the maximum pointing error of NVST on the Coude focal plane is 650.55 arcsec, RMS is 115.88 arcsec, and the 30-minute tracking error is 6.46 arcsec;after model correction, the maximum pointing error is 25.02 arcsec, the RMS value is 3.98 arcsec, and the 30-minute tracking error is 1.10 arcsec. It can be seen that the support vector regression modeling method based on machine learning can effectively improve the pointing and tracking accuracy of the Coude focal plane of altazimuth solar telescope.