Off-axis point spread function characterization in laser guide star adaptive optics systems

Abstract

Adaptive optics (AO) restore the angular resolution of ground-based telescopes, but at the cost of delivering a time-and space-varying point spread function (PSF) with a complex shape. PSF knowledge is crucial for breaking existing limits on the measured accuracy of photometry and astrometry in science observations. In this paper, we concentrate our analyses on anisoplanatism signature only onto PSF: for large-field observations (20") with singleconjugated AO, PSFs are strongly elongated due to anisoplanatism that manifests itself as three different terms for Laser-guide star (LGS) systems: angular, focal and tilt. We propose a generalized model that relies on a point-wise decomposition of the phase and encompasses the non-stationarity of LGS systems. We demonstrate it is more accurate and less computationally demanding than existing models: it agrees with end-to-end physical-optics simulations to within 0.1% of PSF measurables, such as Strehl-ratio, FWHM and fraction of variance unexplained. Secondly, we study off-axis PSF modelling is with respect to C 2 n (h) profile (heights and fractional weights). For 10 m class telescope, PSF morphology is estimated at 1%-level as long as we model the atmosphere with at least 7 layers whose heights and weights are known respectively with 200m and 10%-precision. As a verification test we used the Canada's NRC-Herzberg HeNOS testbed data, featuring four lasers. We highlight capability of retrieving off-axis PSF characteristics within 10% of fraction of variance unexplained, which complies with the expected range from the sensitivity analysis. Our new off-axis PSF modelling method lays the ground-work for testing on-sky in the near future. *

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

“…B ⊥ refers to uncompensated spatial frequencies while B include AO residual only and B∆ corresponds to the anisoplanatism effect. The calculation of this latter can be found in multiple references (Beltramo-Martin et al 2018;Britton 2006;Flicker et al 2003;Fusco et al 2000;Whiteley et al 1998). In the case of SPHERE/ZIMPOL, we must only account for angular anisoplanatism that is produced by the spatial decorrelation of the incoming wavefront.…”

PRIME: PSF Reconstruction and Identification for Multiple-source characterization Enhancement – application to Keck NIRC2 imager

“…with D 0 as the TT excluded residual phase SF in the guide star direction and D ∆ the anisoplanatism SF. A complete methodology to derive D ∆ from the C 2 n (h) is given in ( [3]). D 0 is derived from…”

“…Fig. 4 reports the corresponding errors on PSF estimates, such as the SR, FWHM and Fraction of Variance Unexplained (FVU) that gives the overall PRIME PSF residual from the noise-free reference ( [3]). SR is the most affected PSF estimate and reaches 1% of error for a magnitude 13, for which we get 1% on the altitude seeing estimation and 5% on the other parameters.…”

“…Moreover, the AO telemetry permits to reconstruct the PSF in the Guide Star (GS) direction only. To extrapolate the PSF across the Field of View (FOV), one must model the spatial variations due to anisoplanatism ( [3,10]), whose model relies on atmospheric parameters such as the C 2 n (h). For single-conjugated AO (SCAO) systems that rely on a single Natural Guide Star (NGS) or a Laser Guide Star (LGS) plus a NGS to measure tip-tilt modes, the anisoplanatism model can not be calibrated from AO control loop data, which necessitates to obtain the C 2 n (h) from external instruments ( [22,5,32]) pointing at different sky directions than the telescope.…”

PEPITO: atmospheric Profiling from short-Exposure focal Plane Images in seeing-limiTed mOde

“…D0 depends on integrated values of C 2 n (h) and L0(h) profiles over altitude, not on the specific height distribution. The calculation of D∆ is performed thanks to the anisoplanatism model (Beltramo-Martin et al 2018a) included into the OOMAO simulation framework (Conan & Correia 2014). The algorithm architecture is summarized in Fig.…”