Machine Learning for Improving Stellar Image-based Alignment in Wide-field Telescopes
Zhixu Wu, Yiming Zhang, Rongxin Tang, Zhengyang Li, Xiangyan Yuan, Yong Xia, Hua Bai, Bo Li, Zhou Chen, Xiangqun Cui, Xiaohua Deng
Abstract
Abstract Stellar images will deteriorate dramatically when the sensitive elements of wide-field survey telescopes are misaligned during an observation, and active alignment is the key technology to maintain the high resolution of wide-field sky survey telescopes. Instead of traditional active alignment based on field-dependent wave front errors, this work proposes a machine learning alignment metrology based on stellar images of the scientific camera, which is more convenient and higher speed. We first theoretically confirm that the pattern of the point-spread function over the field is closely related to the misalignment status, and then the relationships are learned by two-step neural networks. After two-step active alignment, the position errors of misalignment parameters are less than 5 μm for decenter and less than 5″ for tip-tilt in more than 90% of the cases. The precise alignment results indicate that this metrology provides a low-cost and high-speed solution to maintain the image quality of wide-field sky survey telescopes during observation, thus implying important significance and broad application prospects.