Physics-based model of the adaptive-optics-corrected point spread function

Fétick, Romain; Fusco, Thierry; Neichel, Benoît; Mugnier, Laurent M.; Beltramo-Martin, Olivier; Bonnefois, Aurélie; Petit, Cyril; Milli, J.; Vernet, J.; Oberti, Sylvain; Bacon, Roland

doi:10.1051/0004-6361/201935830

Cited by 54 publications

(55 citation statements)

References 20 publications

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“…The estimation of r 0 thanks to telemetry data is usually not perfectly accurate and the Fried parameter is often overestimated. However, it has been shown that an AO system can be characterised well to correct for this offset (Fétick et al 2019).…”

Section: Obtaining the Residual Psdmentioning

confidence: 99%

Pyramid wavefront sensor optical gains compensation using a convolutional model

Chambouleyron

Fauvarque

Janin-Potiron

et al. 2020

A&A

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Context. Extremely large telescopes are overwhelmingly equipped with pyramid wavefront sensors (PyWFS) over the more widely used Shack-Hartmann wavefront sensor (SHWFS) to perform their single-conjugate adaptive optics (SCAO) mode. The PyWFS, a sensor based on Fourier filtering, has proven to be highly successful in many astronomy applications. However, this sensor exhibits non-linear behaviours that lead to a reduction of the sensitivity of the instrument when working with non-zero residual wavefronts. This so-called optical gains (OG) effect, degrades the closed-loop performance of SCAO systems and prevents accurate correction of non-common path aberrations (NCPA). Aims. In this paper, we aim to compute the OG using a fast and agile strategy to control PyWFS measurements in adaptive optics closed-loop systems. Methods. Using a novel theoretical description of PyWFS, which is based on a convolutional model, we are able to analytically predict the behaviour of the PyWFS in closed-loop operation. This model enables us to explore the impact of residual wavefront errors on particular aspects such as sensitivity and associated OG. The proposed method relies on the knowledge of the residual wavefront statistics and enables automatic estimation of the current OG. End-to-end numerical simulations are used to validate our predictions and test the relevance of our approach. Results. We demonstrate, using on non-invasive strategy, that our method provides an accurate estimation of the OG. The model itself only requires adaptive optics telemetry data to derive statistical information on atmospheric turbulence. Furthermore, we show that by only using an estimation of the current Fried parameter r 0 and the basic system-level characteristics, OGs can be estimated with an accuracy of less than 10%. Finally, we highlight the importance of OG estimation in the case of NCPA compensation. The proposed method is applied to the PyWFS. However, it remains valid for any wavefront sensor based on Fourier filtering subject from OG variations.

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Section: Obtaining the Residual Psdmentioning

confidence: 99%

Pyramid wavefront sensor optical gains compensation using a convolutional model

Chambouleyron

Fauvarque

Janin-Potiron

et al. 2020

A&A

Self Cite

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“…This model was originally introduced by Fétick et al (2019b) and validated on SPHERE/ZIMPOL and MUSE NFM data. In order to increase the range of applicability of such a model, we upgraded it by including (i) the model of the Apodized Lyot Coronagraph (APLC, Soummer (2005)) used on SPHERE (focal-plane mask not considered) (ii) additional degrees of freedom to adjust static aberrations over a specific modal basis, which can be Zernike modes described on the pupil, tip-tilt and piston modes for each pupil area delimited by the spiders so as to describe the so-called LWE or piston modes for each pupil segment (Laginja et al 2019;Leboulleux et al 2018;Ragland 2018).…”

Section: Psf Modelmentioning

confidence: 99%

“…where D AO , B AO , and W AO are the residual phase structure function, autocovariance function, and PSD respectively, k is the spatial frequencies vector and F [x] is the 2D Fourier transform of x. In Fétick et al (2019b), the PSD is described as a split function to separate the AO-corrected spatial frequencies from the uncorrected frequencies that follow a Kolmogorov's −11/3 power law (Kolmogorov 1941)…”

Section: Psf Modelmentioning

confidence: 99%

“…However, this process lacks science verification and requires years to be fully implemented and op-erational as a stand-alone pipeline. The inherent complexity has inspired several efforts to produce simpler but still efficient reconstruction techniques (Fusco et al 2020;Fétick et al 2019b) that have been fully validated and are now in operation for Multi Unit Spectroscopic Explorer (MUSE) (Bacon et al 2010) at the Very Large Telescope (VLT). The analytic AO PSF model described by Fétick et al (2019b) has shown spectacular accuracy in reproducing the MUSE Narrow Field Mode (NFM) PSF as well as the PSF of the Zurich Imaging Polarimeter (ZIMPOL) (Schmid et al 2018) that equips the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE) instrument (Beuzit et al 2019) PSF and is an excellent candidate to rethink the way we perform PSF reconstruction, with a single flexible algorithm that complies with all kinds of AO correction.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Finally, in order to provide evidence that PRIME is a good solution for PSF-fitting problems post-AO, we have also compared herein results with a Moffat-fitting using the exact same model-fitting process. The Moffat function was defined over 7 parameters (Fétick et al 2019) to allow fitting of asymmetric PSF shape. We report in Fig.…”

Section: Maximizing the Utmost Psf-r Performancementioning

confidence: 99%

Pushing point-spread function reconstruction to the next level: application to SPHERE/ZIMPOL

Beltramo-Martin

Marasco

Fusco

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Point spread function (PSF) reconstruction (PSF-R) is a well established technique to determine reliably and accurately the PSF from Adaptive Optics (AO) control loop data. We have successfully applied this technique to improve the precision on photometry and astrometry to observation of NGC6121 obtained with SPHERE/ZIMPOL as it will be presented in a forthcoming letter. Firstly, we present the methodology we followed to reconstruct the PSF combining pupil-plane and focal-plane measurements using using our PSF-R method PRIME (Beltramo-Martin et al. 2019), with upgrade of both the model and best-fitting steps compared to previous papers. Secondly, we highlight that PRIME allows to maintain the PSF fitting residual below 0.2% over 2 hours of observation and using only 30 s of AO telemetry, which may have important consequences for telemetry storage for PSF-R purpose on future 30-40 m class telescopes. Finally, we deploy PRIME in a more realistic regime using faint stars so as to identify the precision needed on the initial guess parameters to ensure the convergence towards the optimal solution.

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Physics-based model of the adaptive-optics-corrected point spread function

Cited by 54 publications

References 20 publications

Pyramid wavefront sensor optical gains compensation using a convolutional model

Pyramid wavefront sensor optical gains compensation using a convolutional model

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

Pushing point-spread function reconstruction to the next level: application to SPHERE/ZIMPOL

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