MAVIS (the MCAO-Assisted Visible Imager and Spectrograph), planned for the VLT Adaptive Optics Facility, represents an innovative step in Multi-Conjugate Adaptive Optics (MCAO) systems, particularly in its operation at visible wavelengths and anticipated contributions to the field of astronomical astrometry. Recognizing the crucial role of high-precision astrometry in realizing science goals such as studying the dynamics of dense starfields, this study focuses on the challenges of advancing astrometry with MAVIS to its limits, as well as paving the way for further enhancement by incorporating telemetry data as part of the astrometric analysis. We employ MAVISIM, Superstar, and DAOPHOT to simulate both MAVIS imaging performance and provide a pathway to incorporate telemetry data for precise astrometry with MAVIS. Photometry analyses are conducted using the Superstar and DAOPHOT platforms, integrated into a specifically designed pipeline for astrometric analysis in MCAO settings. Combining these platforms, our research aims to elucidate the impact of utilizing telemetry data on improving astrometric precision, potentially establishing new methods for ground-based AO-assisted astrometric analysis. This endeavor not only sheds light on the capabilities of MAVIS but also paves the way for advancing astrometry in the era of next-generation MCAO-enabled giant telescopes.
MAORY is the MCAO module for the ELT. It feeds MICADO and a still to be defined second port instrument. The ”Science Operation” Working Group of MAORY focuses the activity on the simulation of the science cases proposed for the instrument, deriving in this way the achievable performance in different observing conditions, as can be the case of a crowded globular cluster or an almost star-empty frame on a high-z target. In this paper, we discuss the recent activities of the WP focusing on the numerical simulations environment we built and on the contribution to the Operational Concept Description of MAORY.
KEYWORDS: Point spread functions, Adaptive optics, 3D modeling, Data storage, Astronomy, Data processing, Galactic astronomy, Kinematics, Deconvolution, Calibration
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.
Precise stellar photometry and astrometry require the best possible modelling of the point spread function (PSF). To date, the best performances have been obtained when building the PSF a posteriori, meaning directly from the image of dense stellar fields, by exploiting the fact that each star represents a different realisation of the same PSF. The recent advent of the Adaptive Optics technique makes this method more challenging, because of the strong PSF variations across the field of view. One alternative is to use a priori PSF-modelling techniques such as PSF-reconstruction (PSF-R), that rely on Adaptive Optics control loop data to determine the shape of the PSF at any spatial location. Despite being theoretically well established, so far a-priori methods have never surpassed the performance obtained by standard methods when applied to real astronomical imaging. Here we report on the successful use of PRIME, a new technique that combines both PSF-R and image fitting, to perform precise photometry and astrometry on real data of the Galactic globular cluster NGC6121, observed with SPHERE/ZIMPOL. Compared to the results obtained using standard techniques, PRIME achieves improvement in precision by up to a factor of four, and ensures a photometric accuracy within ∼ 0.1 mag. A similar performance is also achieved when using the analytical PSF method described by F´etick et al. 2019, which is specifically designed to model AO-assisted data. These results thus pave the way for the exploitation of innovative techniques to investigate resolved stellar population science cases with the new generation of Adaptive Optics-assisted instrumentation at the ESO’s Very Large Telescope, Keck or the Extremely Large Telescopes.
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