Machine Learning for Improving Stellar Image-based Alignment in Wide-field Telescopes

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.