Vol 22, No 1

Machine Learning for Improving Stellar Image-based Alignment in Widefield Telescopes

Zhixu Wu, Yiming Zhang, Rongxin Tang, Zhengyang Li, Xiangyan Yuan, Yong Xia, Hua Bai, Bo Li, Zhou Chen, Xiang-Qun Cui, Xiaohua Deng


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 twostep neural networks. After two-step active alignment, over 90% of the position errors of misalignment parameters are below 5 μm and over 90% of angle positions are below 5″. 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.


telescopes – techniques: high angular resolution – techniques: miscellaneous

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