OUP accepted manuscript

Jia, Peng; Basden, Alastair; Osborn, James

doi:10.1093/mnras/sty1507

Cited by 7 publications

(1 citation statement)

References 50 publications

(41 reference statements)

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“…The method proposed in this paper, along with realistic atmospheric turbulence phase screen generation (Jia et al 2015a,b;Buscher 2016) and observation real-time simulation methods (Wang 2012), can be used to get high-fidelity simulation of AO (Carbillet et al 2004;Basden et al 2007;Le Louarn et al 2012;Conan & Correia 2014;Basden et al 2018). We can also use this method for AO concept testing (Zhang, Guo & Rao 2017), observation strategy design (Jia, Basden & Osborn 2018), even modelling (Peterson et al 2015) and analysing point spread function (PSF) temporal variation in sky surveying telescopes (York et al 2000;Ivezic et al 2008;Dark Energy Survey Collaboration et al 2016), such as SDSS PSF study carried out by Xin et al (2018). The GMM model as a description method can be used with other methods (Chen et al 2016) to help us better analyse the properties of the TP.…”

Section: Introductionmentioning

confidence: 97%

Modelling synthetic atmospheric turbulence profiles with temporal variation using Gaussian mixture model

Jia

Osborn

Kong

et al. 2018

Monthly Notices of the Royal Astronomical Society

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The atmospheric turbulence profile plays a very important role for performance evaluation of wide-field adaptive optic systems. Since the atmospheric turbulence is evolving, the turbulence profile will change with time. To better model the temporal variation of turbulence profile, in this paper, we propose to use the extensive stereo-SCIDAR turbulence profile dataset from one observation site to train a Gaussian mixture model. The trained Gaussian mixture model can describe the structure of the turbulence profile in that particular site with several multidimensional Gaussian distributions. We cluster the turbulence profile data with the Gaussian mixture model and analyse the temporal variation properties of the clusters. We define the characteristic time as the time that the measured turbulence profile remains in a given profile. We find that normally the characteristic time is around 2 to 20 min and will change at different sites and in different seasons. With the statistical results of the characteristic time and the trained Gaussian mixture model, we can generate synthetic artificial turbulence profiles with realistic temporal variation to better test the performance of adaptive optics systems.

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Section: Introductionmentioning

confidence: 97%

Modelling synthetic atmospheric turbulence profiles with temporal variation using Gaussian mixture model

Jia

Osborn

Kong

et al. 2018

Monthly Notices of the Royal Astronomical Society

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Compressive Shack–Hartmann wavefront sensor based on deep neural networks

Jia

Cai

et al. 2021

Monthly Notices of the Royal Astronomical Society

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The Shack–Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However, if strong atmospheric turbulence exists or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this work, we propose a compressive Shack–Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all subapertures, our method reconstructs wavefronts with slope measurements of subapertures that have spot images with high signal-to-noise ratio. We further propose to use a deep neural network to accelerate the wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase the development speed of our method. After training, the compressive Shack–Hartmann wavefront sensing method can reconstruct wavefronts at high spatial resolution with slope measurements from only a small number of subapertures. We integrate the straightforward compressive Shack–Hartmann wavefront sensing method with an image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our compressive Shack–Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications.

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Correction performance uniformity on ground-layer adaptive optics

Yang

Zhang

Rao

2022

Monthly Notices of the Royal Astronomical Society

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Ground-layer adaptive optics (GLAO) is generally used to improve the seeing for large-aperture ground-based optical telescopes across a wide field of view with uniform imaging quality. In this paper, a new criterion, which is defined as the weighted sum of the mean and mean-square-error values of the full width at half-maximum of the field-dependent point spread function of the image over the whole field of view, is proposed to evaluate GLAO performance uniformity. An analytic model of the GLAO-corrected point spread function based on spatial filtering methods is used to investigate the uniformity and image quality over the whole field of view for different guide-star layouts, telescope apertures, numbers of actuators for the deformable mirror, and turbulence profiles. Simulations are performed on the GLAO configurations for the 1-m New Vacuum Solar Telescope at Fuxian Solar Observatory. The results show that, compared with that of the classical evaluation, the correction uniformity improves by 63 per cent, with the resolution sacrificing only 5 per cent for a measured turbulence profile at Fuxian Solar Observatory. Furthermore, this criterion is applied to various requirements, and some critical system parameters are suggested for the wide field-of-view GLAO.

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OUP accepted manuscript

Cited by 7 publications

References 50 publications

Modelling synthetic atmospheric turbulence profiles with temporal variation using Gaussian mixture model

Modelling synthetic atmospheric turbulence profiles with temporal variation using Gaussian mixture model

Compressive Shack–Hartmann wavefront sensor based on deep neural networks

Correction performance uniformity on ground-layer adaptive optics

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