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

Beltramo-Martin, Olivier; Marasco, Antonino; Fusco, Thierry; Massari, D.; Milli, J.; Fiorentino, G.; Neichel, Benoît

doi:10.1093/mnras/staa525

Cited by 8 publications

(5 citation statements)

References 49 publications

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“…Current galaxy imaging surveys, such as DES (Jarvis et al 2016), KIDS (Kuijken et al 2015), and CFIS (Ibata et al 2017), as well as future surveys, such as the Vera C. Rubin Observatory's LSST (Tyson et al 2006), the Euclid mission (Laureijs et al 2011), or the Roman Space Telescope, require an estimated determination of the instrument's point spread function (PSF). For some scientific applications, such as weak gravitational lensing (Kilbinger 2015), low-surface brightness studies (Infante-Sainz et al 2019), or analyses of diffraction-limited images in crowded stellar fields (Beltramo-Martin et al 2020), the PSF must be reconstructed with a high level of accuracy. A preliminary approach is to derive a PSF model using available information about the instrument, whereupon the model parameters are then chosen by fitting observed stars in the field to yield a PSF model.…”

Section: Introductionmentioning

confidence: 99%

Multi-CCD modelling of the point spread function

Liaudat

Bonnin

Starck

et al. 2021

A&A

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Context. Galaxy imaging surveys observe a vast number of objects, which are ultimately affected by the instrument’s point spread function (PSF). It is weak lensing missions in particular that are aimed at measuring the shape of galaxies and PSF effects represent an significant source of systematic errors that must be handled appropriately. This requires a high level of accuracy at the modelling stage as well as in the estimation of the PSF at galaxy positions. Aims. The goal of this work is to estimate a PSF at galaxy positions, which is also referred to as a non-parametric PSF estimation and which starts from a set of noisy star image observations distributed over the focal plane. To accomplish this, we need our model to precisely capture the PSF field variations over the field of view and then to recover the PSF at the chosen positions. Methods. In this paper, we propose a new method, coined Multi-CCD (MCCD) PSF modelling, which simultaneously creates a PSF field model over the entirety of the instrument’s focal plane. It allows us to capture global as well as local PSF features through the use of two complementary models that enforce different spatial constraints. Most existing non-parametric models build one model per charge-coupled device, which can lead to difficulties in capturing global ellipticity patterns. Results. We first tested our method on a realistic simulated dataset, comparing it with two state-of-the-art PSF modelling methods (PSFEx and RCA) and finding that our method outperforms both of them. Then we contrasted our approach with PSFEx based on real data from the Canada-France Imaging Survey, which uses the Canada-France-Hawaii Telescope. We show that our PSF model is less noisy and achieves a ∼22% gain on the pixel’s root mean square error with respect to PSFEx. Conclusions. We present and share the code for a new PSF modelling algorithm that models the PSF field on all the focal plane that is mature enough to handle real data.

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Section: Introductionmentioning

confidence: 99%

Multi-CCD modelling of the point spread function

Liaudat

Bonnin

Starck

et al. 2021

A&A

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“…The PSF-R software we have used is able to reproduce the observed FWHM, EE, and SR with an accuracy of 2% to 4%, which is comparable to the level of accuracy reached by hybrid or calibrated techniques (see e.g., PRIME 29,30 ). Moreover, the reconstructed PSF provides a better fit to the observed data than the diffraction limited one.…”

Section: Discussionmentioning

confidence: 55%

Point spread function reconstruction for SOUL + LUCI LBT data

Simioni

Arcidiacono

Wagner

et al. 2022

J. Astron. Telesc. Instrum. Syst.

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Here, we present the status of an ongoing project aimed at developing a point spread function (PSF) reconstruction software for adaptive optics (AO) observations. In particular, we test for the first time the implementation of pyramid wave-front sensor data on our algorithms. As a first step in assessing its reliability, we applied the software to bright, on-axis, point-like sources using two independent sets of observations, acquired with the single-conjugated AO upgrade for the Large Binocular Telescope. Using only telemetry data, we reconstructed the PSF by carefully calibrating the instrument response. The accuracy of the results has been first evaluated using the classical metric: specifically, the reconstructed PSFs differ from the observed ones by <2% in Strehl ratio and 4.5% in full-width at half maximum. Moreover, the recovered encircled energy associated with the PSF core is accurate at 4% level in the worst case. The accuracy of the reconstructed PSFs has then been evaluated by considering an idealized scientific test-case consisting in the measurements of the morphological parameters of a compact galaxy. In the future, our project will include the analysis of anisoplanatism, low signal-to-noise ratio regimes, and the application to multi-conjugated AO observations.

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“…We give in the current section an overview on recent approaches that were developed and successfully validated on-sky, which are the analytical PSF model presented in, 21,22 the PSF reconstruction [23][24][25][26] and the hybrid PSF reconstruction. 27,28…”

Section: Landscapementioning

confidence: 99%

“…On data acquired with SPHERE/ZIMPOL in V and NIRC2 at Keck in H/K, a 1% of error on the 2D PSF estimation has been obtained. 27,28 Hybrid techniques are particularly well suited to science cases that offer access to stars in the field.…”

Section: Hybrid Reconstructionmentioning

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

Cited by 8 publications

References 49 publications

Multi-CCD modelling of the point spread function

Multi-CCD modelling of the point spread function

Point spread function reconstruction for SOUL + LUCI LBT data

Review of PSF reconstruction methods and application to post-processing

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