PSF-NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes
Jia, Peng ; Wu, Xuebo ; Yi, Huang ; Cai, Bojun ; Cai, Dongmei
Ground-based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instrument design and image restoration method development. Because the point-spread function can reflect performance of the whole optic system, it is appropriate to use the point-spread function to describe atmospheric turbulence induced aberrations. Assuming point-spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a nonparametric point-spread function -- PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would benefit the development of new observation methods for ground-based optical telescopes.
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Paper Information
Paper Title:
PSF-NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes
Publication:
The Astronomical Journal
Bibcode:
2020AJ....159..183J
DOI:
10.3847/1538-3881/ab7b79
Identifier
DOI:
10.12149/101015
VO Identifier:
ivo://China-VO/paperdata/101015
Publication date:
2020-01-10
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This DOI represents all versions, and will always resolve to the latest one.
2020-01-10