Online estimation of the wavefront outer scale profile from adaptive optics telemetry

Guesalaga, Andrés; Neichel, Benoît; Correia, Carlos; Butterley, T.; Osborn, James; Masciadri, E.; Fusco, Thierry; Sauvage, J.-F.

doi:10.1093/mnras/stw2548

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…Another thing to note in (14) is that we still represent the PSD as a sum of radial basis functions, which does introduce some small amount of modeling error. This is done purely for computational reasons, since calculating the term corresponding to Bφ(c, γ) exactly would require an expensive Fourier transform, which would take the runtime of fmincon from seconds to hours on a desktop computer.…”

Section: Methods 1: Parametric Power Lawmentioning

confidence: 99%

“…The first approach deduces the profile from the cross-correlations by deconvolution with the autocorrelation of the data [37,36]. The second approach [14,12,9,15,6,35] forms a linear dependency between the spatial cross-correlations and the vertical turbulence profile by assuming that the turbulence statistics at any altitude is accurately described by the Kolmogorov or von Kármán model [27,21,22]. This paper grows out of a number of experiments which confirm significant deviations of the turbulence statistics from the classical models in certain portions of the atmosphere [28,38,29,2].…”

Section: Introductionmentioning

confidence: 99%

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Atmospheric turbulence profiling with unknown power spectral density

et al. 2018

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Adaptive optics (AO) is a technology in modern ground-based optical telescopes to compensate for the wavefront distortions caused by atmospheric turbulence. One method that allows to retrieve information about the atmosphere from telescope data is so-called SLODAR, where the atmospheric turbulence profile is estimated based on correlation data of Shack-Hartmann wavefront measurements. This approach relies on a layered Kolmogorov turbulence model. In this article, we propose a novel extension of the SLODAR concept by including a general non-Kolmogorov turbulence layer close to the ground with an unknown power spectral density. We prove that the joint estimation problem of the turbulence profile above ground simultaneously with the unknown power spectral density at the ground is illposed and propose three numerical reconstruction methods. We demonstrate by numerical simulations that our methods lead to substantial improvements in the turbulence profile reconstruction compared to the standard SLODAR-type approach. Also, our methods can accurately locate local perturbations in non-Kolmogorov power spectral densities.

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Section: Methods 1: Parametric Power Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Atmospheric turbulence profiling with unknown power spectral density

et al. 2018

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“…We present in this section the most recent advances made for the characterization step, which are related to the upgrade by Guesalaga et al 17 of the SLODAR-type (SLOpe Detection And Ranging) turbulence profiler in order to estimate non-uniform profiles of the outer scale of the turbulence, L 0 (h). This SLODAR profiler was originally developed for GeMS (Gemini South Multiconjugate AO system) 18 and the upgraded version is now currently tested on the Adaptive Optics Facility (AOF) at the Very Large Telescope (VLT).…”

Section: Advances In Step 1: Profiling the Outer Scale Of Turbulencementioning

confidence: 99%

PSF reconstruction via full turbulence characterization and end-to-end simulations

BÃ©chet¹,

AyancÃ¡n²,

Guesalaga³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

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Enhancement and wise archiving of astronomical images require an accurate estimate of the observational Point Spread Function (PSF). Although modelling of the telescope and its optics is a well-understood problem, PSF reconstruction becomes challenging when the observations include adaptive optics (AO) correction. The approach presented in this paper consists in feeding an end-to-end (E2E) simulation of the telescope, the instrument and its environment with the characterized disturbances from the telemetry and AO loop data, in order to produce the estimated PSFs. This method benefits from the developments made in the last years with respect to the estimation of external disturbances during AO correction, such as turbulence profile and its dynamics as well as sensor noise and vibrations characteristics. In particular, characterization of the turbulence profile in terms of strength, C 2 n (h), and outer scale, L 0 (h), is considered with an example on on-sky recorded AO telemetry from the GALACSI AO system. Once identified, the internal and external parameters of the observing conditions are used as inputs to carry out E2E simulations of the optical propagation and estimate the PSF. The method can be regarded as a "brute force" approach, as it is highly intensive in computer power; particularly for the ELTs. However, its ability to integrate complex combination of effects from all disturbances and not relying on analytical approximations for the aliasing or fitting errors makes the approach worth of a deeper study. E2E simulations have been used before in PSF reconstruction, but limited to a theoretical modelling of the system. Here, the development of the E2E simulation part is an ongoing work. A simplified AO system similar to the GALACSI WFM is currently simulated to obtain the PSF estimates and illustrate how such approach allows to account for the anisoplanatic effects and for the influence of the outer scale values.

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“…Dedicated instruments exist to monitor this profile [17,3,24], but they aim at characterising the observation site in terms of atmosphere quality and do not observe in the telescope line of sight. Consequently, their estimated profile deviate up to at least 10 % [16], from AO telemetry-based approaches available for multi-GS AO systems [10,9,11,12,15]. However, according to [1], 10% of error on the C 2 n (h) estimation may degrade the photometry and astrometry determination up to respectively 3% and 300 µas in a 20"-FOV, while we are seeking to reach better than 1% and 150 µas.…”

Section: Introductionmentioning

confidence: 96%

Focal-plane Cn2(h) profiling based on single-conjugate adaptive optics compensated images

Beltramo-Martin

Correia

Neichel

et al. 2018

Monthly Notices of the Royal Astronomical Society

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Knowledge of the atmospheric turbulence in the telescope line-of-sight is crucial for wide-field observations assisted by adaptive optics (AO), for which the Point Spread Function (PSF) becomes strongly elongated due to the anisoplanatism effect. This one must be modelled accurately to extrapolate the PSF anywhere across the Field of view (FOV) and improve the science exploitation. However, anisoplanatism is a function of the C 2 n (h) profile, that is not directly accessible from single conjugate AO telemetry. One may rely on external profilers, but recent studies have highlighted more than 10% of discrepancies with AO internal measurements, while we aim better than 1% of accuracy for PSF modelling. To tackle this existing limitation, we present the Focal plane profiling (FPP), as a C 2 n (h) profiling method that relies on post-AO focal plane images. We demonstrate such an approach complies with a 1%-level of accuracy on the C 2 n (h) estimation and establish how this accuracy varies regarding the calibration stars magnitudes and positions in the field. We highlight that photometry and astrometry errors due to PSF mis-modelling reaches respectively 1% and 50µas using FPP on a Keck baseline, with a preliminary calibration using a star of magnitude H=14 at 20". We also validate this concept using Canadas NRC-Herzberg HENOS testbed images in comparing FPP retrieval with alternative C 2 n (h) measurements on HeNOS. The FPP approach allows to profile the C 2 n (h) using the SCAO systems and improve significantly the PSF characterisation. Such a methodology is also ELT-size compliant and will be extrapolated to tomographic systems in a near future. *

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Online estimation of the wavefront outer scale profile from adaptive optics telemetry

Cited by 12 publications

References 21 publications

Atmospheric turbulence profiling with unknown power spectral density

Atmospheric turbulence profiling with unknown power spectral density

PSF reconstruction via full turbulence characterization and end-to-end simulations

Focal-plane Cn2(h) profiling based on single-conjugate adaptive optics compensated images

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