PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Jia, Peng; Wu, Xue-Bing; Huang, Yi; Cai, Bin; Cai, Dongmei

doi:10.3847/1538-3881/ab7b79

Cited by 24 publications

(19 citation statements)

References 57 publications

(40 reference statements)

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“…Wide field adaptive optics system, such as ground layer adaptive optics system could then use natural guide stars as wavefront references for observations (Rigaut 2002;Nicolle et al 2004b;Travouillon et al 2004;Chun et al 2016;Carbillet et al 2017;Jia et al 2018;Lu et al 2018b). In our future works, we will integrate the CS-WFS with the deep learning based PSF model (Jia et al 2020) to develop high-order PSF reconstruction method and we will also further investigate applications of the compressive wavefront sensing in wide field adaptive optics systems.…”

Section: Discussionmentioning

confidence: 99%

Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

Jia,

Ma,

Cai

et al. 2020

Preprint

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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 there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all sub-apertures, our method reconstructs wavefronts with slope measurements of sub-apertures which have spot images with high signal to noise ratio. Besides, we further propose to use a deep neural network to accelerate 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 development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts in high spatial resolution with slope measurements from only a small amount of sub-apertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with 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 the 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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Section: Discussionmentioning

confidence: 99%

Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

Jia,

Ma,

Cai

et al. 2020

Preprint

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“…1. In this paper, we split the static aberration contribution in two terms: the Non Common Path Aberrations (NCPA) calibration (non-coronagraphic) noted ∆ NCPA (Jia et al 2020;Vigan et al 2019;Lamb et al 2018;Vassallo et al 2018;N'Diaye et al 2016;Sitarski et al 2014;Jolissaint et al 2012;Sauvage et al 2011;Robert et al 2008;Mugnier et al 2008;Blanc et al 2003;Fusco et al 2003), to which is added another contribution decomposed over a modal basis M to be specified. This latter could be simply Zernike modes over the whole pupil or per pupil area delimited by the spiders, so as to include potential LWE that may introduce severe asymmetries in the PSF structure.…”

Section: Psf Modelmentioning

confidence: 99%

Joint estimation of atmospheric and instrumental defects using a parsimonious point spread function model

Beltramo-Martin

Fétick

Neichel

et al. 2020

A&A

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Context. Modeling the optical point spread function (PSF) is particularly challenging for adaptive optics (AO)-assisted observations owing to the its complex shape and spatial variations. Aims. We aim to (i) exhaustively demonstrate the accuracy of a recent analytical model from comparison with a large sample of imaged PSFs, (ii) assess the conditions for which the model is optimal, and (iii) unleash the strength of this framework to enable the joint estimation of atmospheric parameters, AO performance and static aberrations. Methods. We gathered 4812 on-sky PSFs obtained from seven AO systems and used the same fitting algorithm to test the model on various AO PSFs and diagnose AO performance from the model outputs. Finally, we highlight how this framework enables the characterization of the so-called low wind effect on the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (LWE; SPHERE) instrument and piston cophasing errors on the Keck II telescope. Results. Over 4812 PSFs, the model reaches down to 4% of error on both the Strehl-ratio (SR) and full width at half maximum (FWHM). We particularly illustrate that the estimation of the Fried’s parameter, which is one of the model parameters, is consistent with known seeing statistics and follows expected trends in wavelength using the Multi Unit Spectroscopic Explorer instrument (λ6/5) and field (no variations) from Gemini South Adaptive Optics Imager images with a standard deviation of 0.4 cm. Finally, we show that we can retrieve a combination of differential piston, tip, and tilt modes introduced by the LWE that compares to ZELDA measurements, as well as segment piston errors from the Keck II telescope and particularly the stair mode that has already been revealed from previous studies. Conclusions. This model matches all types of AO PSFs at the level of 4% error and can be used for AO diagnosis, post-processing, and wavefront sensing purposes.

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“…[9][10][11][12] A recent paper 13 deals with some of the same topics as the present publication but focuses on laser communication with monochromatic light and does not touch upon the issues of phase screen generation, noise, blurring by wide-band imaging, closed-loop operation, and bit depth, which all are important for our application. Another recent article 14 briefly describes an interesting image sharpening approach using a neural network based on unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

Image-based wavefront sensing for astronomy using neural networks

Andersen

Owner-Petersen

Enmark

2020

J. Astron. Telesc. Instrum. Syst.

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Motivated by the potential of nondiffraction limited, real-time computational image sharpening with neural networks in astronomical telescopes, we studied wavefront sensing with convolutional neural networks based on a pair of in-focus and out-of-focus point spread functions. By simulation, we generated a large dataset for training and validation of neural networks and trained several networks to estimate Zernike polynomial approximations for the incoming wavefront. We included the effect of noise, guide star magnitude, blurring by wide-band imaging, and bit depth. We conclude that the "ResNet" works well for our purpose, with a wavefront RMS error of 130 nm for r 0 ¼ 0.3 m, guide star magnitudes 4 to 8, and inference time of 8 ms. It can also be applied for closed-loop operation in an adaptive optics system. We also studied the possible use of a Kalman filter or a recurrent neural network and found that they were not beneficial to the performance of our wavefront sensor. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Cited by 24 publications

References 57 publications

Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

Joint estimation of atmospheric and instrumental defects using a parsimonious point spread function model

Image-based wavefront sensing for astronomy using neural networks

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