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Since 2005 ESO has been working with its community and industry to develop an extremely large optical/infrared telescope. ESO's Extremely Large Telescope, or ELT for short, is a revolutionary ground-based telescope that will have a 39-metre main mirror and will be the largest visible and infrared light telescope in the world. To address specific topics that are needed for the science operations and calibrations of the telescope, thirteen specific working groups were created to coordinate the effort between ESO, the instrument consortia, and the wider community. We describe here the goals of these working groups as well as their achievements so far., Comment: 8 pages, 4 figures

We use spectra observed with the integral-field spectrograph MUSE to reveal the central kinematics of the Galactic globular cluster Messier 80 (M80, NGC 6093). Using observations obtained with the recently commissioned narrow-field mode of MUSE, we are able to analyse 932 stars in the central 7.5 arcsec by 7.5 arcsec of the cluster for which no useful spectra previously existed. Mean radial velocities of individual stars derived from the spectra are compared to predictions from axisymmetric Jeans models, resulting in radial profiles of the velocity dispersion, the rotation amplitude, and the mass-to-light ratio. The new data allow us to search for an intermediate-mass black hole (IMBH) in the centre of the cluster. Our Jeans model finds two similarly probable solutions around different dynamical cluster centres. The first solution has a centre close to the photometric estimates available in the literature and does not need an IMBH to fit the observed kinematics. The second solution contains a location of the cluster centre that is offset by about 2.4 arcsec from the first one and it needs an IMBH mass of $4600^{+1700}_{-1400}$ solar masses. N-body models support the existence of an IMBH in this cluster with a mass of up to 6000 solar masses in this cluster, although models without an IMBH provide a better fit to the observed surface brightness profile. They further indicate that the cluster has lost nearly all stellar-mass black holes. We further discuss the detection of two potential high-velocity stars with radial velocities of 80 to 90 km/s relative to the cluster mean., Comment: accepted for publication in MNRAS; data can be found here: https://doi.org/10.25625/VCNHOR

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. 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. 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 SPHERE instrument and piston cophasing errors on the Keck II telescope. 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 parameter, which is one of the model parameters, is consistent with known seeing statistics and follows expected trends in wavelength using the MUSE instrument ($\lambda^{6/5}$) and field (no variations) from GSAOI images with a standard deviation of 0.4cm. 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. 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.

One of the major limitations of adaptive optics (AO) corrected image post-processing is the lack of knowledge on the system point spread function (PSF). The PSF is not always available as a direct imaging on isolated point like objects such as stars. Its prediction using AO telemetry also suffers from serious limitations and requires complex and yet not fully operational algorithms. A very attractive solution consists in a direct PSF estimation using the scientific images themselves thanks to blind or myopic post-processing approaches. We demonstrate that such approaches suffer from severe limitations when a joint restitution of object and PSF parameters is performed. As an alternative we propose here a marginalized PSF identification that overcomes this limitation. Then the PSF is used for image post-processing. Here we focus on deconvolution, a post-processing technique to restore the object, given the image and the PSF. We show that the PSF estimated by marginalisation provides good quality deconvolution. The full process of marginalized PSF estimation and deconvolution constitutes a successful blind deconvolution technique. It is tested on simulated data to measure its performance. It is also tested on experimental adaptive optics images of the asteroid 4-Vesta by VLT/SPHERE/Zimpol to demonstrate application to on-sky data.

Context. Adaptive optics (AO) systems greatly increase the resolution of large telescopes, but produce complex point spread function (PSF) shapes, varying in time and across the field of view. This PSF must be accurately known since it provides crucial information about optical systems for design, characterisation, diagnostics and image post processing. Aims. We develop here a model of the AO long exposure PSF, adapted to various seeing conditions and any AO system. This model is made to match accurately both the core of the PSF and its turbulent halo. Methods. The PSF model we develop is based on a parsimonious parameterization of the phase power spectral density with only five parameters to describe circularly symmetric PSFs and seven parameters for asymmetrical ones. Moreover, one of the parameters is directly the Fried parameter r0 of the turbulence s strength. This physical parameter is an asset in the PSF model since it can be correlated with external measurements of the r0, such as phase slopes from the AO real time computer (RTC) or site seeing monitoring. Results. We fit our model against endtoend simulated PSFs using OOMAO tool, and against on sky PSFs from the SPHERE ZIMPOL imager and the MUSE integral field spectrometer working in AO narrowfield mode. Our model matches the shape of the AO PSF both in the core and the halo, with a sub 1 percent relative error for simulated and experimental data. We also show that we retrieve the r0 parameter with subcentimeter precision on simulated data. For ZIMPOL data, we show a correlation of 97 percent between our r0 estimation and the RTC estimation. Finally, MUSE allows us to test the spectral dependency of the fitted r0 parameter. It follows the theoretical $\lambda^{6/5}$ evolution with a standard deviation of 0.3 cm. Evolution of other PSF parameters, such as residual phase variance or aliasing, is also discussed.