Exploring the impact of PSF reconstruction errors on the reduction of astronomical adaptive optics based data

Jolissaint, Laurent; Carfantan, Hervé; Anterrieu, Éric

doi:10.1117/12.789618

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Some work have been conducted in the past to address this question. 55,56 In the case where the PSF is determined from an analytical model of the residual phase PSD (Fourier, covariance model-based), the PSF uncertainties are directly driven by the uncertainties on the model inputs parameters (seeing, C 2 n (h), L 0 ,...) and one may easily establish this connection. Note that biases are (must be) compensated by comparing the PSF estimator with on-sky observations.…”

Section: Discussion On Uncertaintiesmentioning

confidence: 99%

Review of PSF reconstruction methods and application to post-processing

Beltramo-Martin

Ragland

Fétick

et al. 2020

Adaptive Optics Systems VII

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Determining the PSF remains a key challenge for post adaptive-optics (AO) observations regarding the spatial, temporal and spectral variabilities of the AO PSF, as well as itx complex structure. This paper aims to provide a non-exhaustive but classified list of techniques and references that address this issue of PSF determination, with a particular scope on PSF reconstruction, or more generally pupil-plane-based approaches. We have compiled a large amount of references to synthesize the main messages and kept them at a top level. We also present applications of PSF reconstruction/models to post-processing, more especially PSF-fitting and deconvolution for which there is a fast progress in the community.

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Section: Discussion On Uncertaintiesmentioning

confidence: 99%

Review of PSF reconstruction methods and application to post-processing

Beltramo-Martin

Ragland

Fétick

et al. 2020

Adaptive Optics Systems VII

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“…Deconvolution requires knowledge of the PSF, and the quality of the deconvolved image directly depends on the accuracy of its PSF model (Jolissaint et al 2008;Davies & Kasper 2012). For example, Fétick et al (2019a) and Marchis et al (2021) used observations of asteroids from the European Southern Observatory (ESO) Large Program (ID 199.C-0074, PI P. Vernazza, Vernazza et al (2021)) to show that the classical method of getting reference PSFs by observing calibration stars before or after the science observations leads to unacceptable errors in more than 50% of the cases.…”

Section: Introductionmentioning

confidence: 99%

Improved prior for adaptive optics point spread function estimation from science images: Application for deconvolution

Lau

Fétick

et al. 2023

A&A

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Context. Access to knowledge of the point spread function (PSF) of adaptive optics(AO)-assisted observations is still a major limitation when processing AO data. This limitation is particularly important when image analysis requires the use of deconvolution methods. As the PSF is a complex and time-varying function, reference PSFs acquired on calibration stars before or after the scientific observation can be too different from the actual PSF of the observation to be used for deconvolution, and lead to artefacts in the final image. Aims. We improved the existing PSF-estimation method based on the so-called marginal approach by enhancing the object prior in order to make it more robust and suitable for observations of resolved extended objects. Methods. Our process is based on a two-step blind deconvolution approach from the literature. The first step consists of PSF estimation from the science image. For this, we made use of an analytical PSF model, whose parameters are estimated based on a marginal algorithm. This PSF was then used for deconvolution. In this study, we first investigated the requirements in terms of PSF parameter knowledge to obtain an accurate and yet resilient deconvolution process using simulations. We show that current marginal algorithms do not provide the required level of accuracy, especially in the presence of small objects. Therefore, we modified the marginal algorithm by providing a new model for object description, leading to an improved estimation of the required PSF parameters. Results. Our method fulfills the deconvolution requirement with realistic system configurations and different classes of Solar System objects in simulations. Finally, we validate our method by performing blind deconvolution with SPHERE/ZIMPOL observations of the Kleopatra asteroid.

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“…We say "almost" because when a mismatched PSF is used for deconvolution the final image will in the most optimistic scenario not improve, and in most cases it will look worse than the raw data. Especially in the astronomical community the task of measuring the PSF simultaneously with the scientific observations has been given a lot of attention [1][2][3]. The problem also has relevance for surveillance & reconnaissance [4] as well as imaging of space-based assets [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive optics point spread function reconstruction directly from target data

Gładysz

2016

Imaging and Applied Optics 2016

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Reconstruction of point spread function (PSF) using information from the wavefront sensor is a classic problem in adaptive optics. Although the theory and methods for this task have been in use for the last twenty years, implementation and application of the classic PSF reconstruction still proves difficult and will become even more formidable for future adaptive optics systems. Here we present an alternative solution which does not require any extra information from the wavefront sensor, nor does it rely on knowledge of the turbulence profile; the method requires a sequence of images of the same object

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Exploring the impact of PSF reconstruction errors on the reduction of astronomical adaptive optics based data

Cited by 5 publications

References 15 publications

Review of PSF reconstruction methods and application to post-processing

Review of PSF reconstruction methods and application to post-processing

Improved prior for adaptive optics point spread function estimation from science images: Application for deconvolution

Adaptive optics point spread function reconstruction directly from target data

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