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1 May 2020 Analytic approach for segment-level wavefront sensing in the global alignment progress based on image feature analysis
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Segmented mirror space telescopes have many advantages in both observation capacity and engineering feasibility. However, the alignment procedures for them are particularly complicated. Meanwhile, global alignment is one of the most important steps, in which the misalignments of each segment should be determined and corrected before image stacking is performed. Therefore, segment-level wavefront sensing is needed in this process. At present, traditional iterative phase retrieval algorithm is used to recover the segmented-level wavefront phase. However, the efficiency of this algorithm is comparatively low, especially given that there is an array of segment-level wavefront maps that need to be recovered. In addition, the magnitudes of misalignments are comparatively large in this stage and the iterative phase retrieval algorithm can be trapped in a local minimum for large-scale wavefront sensing. An analytic approach is proposed to estimate the segment-level wavefront aberrations based on the analysis of the geometrical features of one defocused point spread function (PSF) image. Meanwhile, some aberration properties of the misaligned system are also utilized. Simulations and an experiment are performed to verify the effectiveness of the proposed approach. This work can not only improve the efficiency and robustness of the global alignment of segmented mirror space telescopes, but also provide an intuitive and in-depth understanding for the mechanism of aberration calculation using PSF image features.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4124/2020/$28.00 © 2020 SPIE
Guohao Ju, Boqian Xu, Di Zhang, Hongcai Ma, and Xiaoquan Bai "Analytic approach for segment-level wavefront sensing in the global alignment progress based on image feature analysis," Journal of Astronomical Telescopes, Instruments, and Systems 6(2), 029003 (1 May 2020).
Received: 10 December 2019; Accepted: 8 April 2020; Published: 1 May 2020

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