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

Abstract

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.

DEEPLOOP: DEEP Learning for an Optimized adaptive Optics Psf estimation

“…We have used DEEPLOOP on PSFs simulations generated from the segmented and AO-assisted Keck-II telescope. The PSFs were simulated with the PSFAO21 model [1], which is parametric and includes 6 parameters to describe the atmospheric contribution, as well as 9 additional parameters to describe the mirror phase errors of the Keck-II primary mirror. Therefore, the resulting PSF is : P SF = P SF atm N P SF tel , P SF atm was generated from the Power Spectrum Density (PSD) described in [2].…”

“…DEEPLOOP was originally designed to enhance from an image, the retrieval of the parameters of the PSFAO21 model [1], in comparison to PSF-fitting approaches. In this section, we will take an example from the previous issue and describe some of the main parameters you need to define when you run a simulation on a node of GPUs.…”

Review of PSF reconstruction methods and application to post-processing

“…For over 4,800 PSFs obtained on these instruments, the model succeeds in reproducing the PSF metrics (Strehlratio, FWHM) at less than 4% or error. 21 Note that this focal-plane-based/pupil-plane-based dual aspect could be also true for the Fourier-based model; we could distinguish a few parameters in this model to be retrieved on the image. Nevertheless, building such a model already requires AO expertise to describe the propagation of spatial frequencies, while the PSFAO19 model is a concise and parsimonious representation of the known AO PSD behavior, and still guarantee an excellent level of accuracy.…”

“…The PSF determination technique that works best the exploitation of a given astronomical image is obviously science-case dependent. 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…”

“…This is hypothetically due to lack of access to on-sky AO telemetry data from such operating systems (MUSE NFM, GeMS) and the need of complex recipes to model the PSF across the field. 45 Note that this strategy is also envisioned for solar AO systems * • PSFAO19 model A recent analytical description has been proposed 21,22 to represent the PSD/PSF from physical-based parameters. This model jointly describes both the corrected and uncorrected parts of the PSF, with successful recent applications.…”