Context. Differentiating between a true exoplanet signal and residual speckle noise is a key challenge in high-contrast imaging (HCI). Speckles result from a combination of fast, slow, and static wavefront aberrations introduced by atmospheric turbulence and instrument optics. While wavefront control techniques developed over the last decade have shown promise in minimizing fast atmospheric residuals, slow and static aberrations such as non-common path aberrations (NCPAs) remain a key limiting factor for exoplanet detection. NCPAs are not seen by the wavefront sensor (WFS) of the adaptive optics (AO) loop, hence the difficulty in correcting them. Aims. We propose to improve the identification and rejection of slow and static speckles in AO-corrected images. The algorithm known as the Direct Reinforcement Wavefront Heuristic Optimisation (DrWHO) performs a frequent compensation operation on static and quasi-static aberrations (including NCPAs) to boost image contrast. It is applicable to general-purpose AO systems as well as HCI systems. Methods. By changing the WFS reference at every iteration of the algorithm (a few tens of seconds), DrWHO changes the AO system point of convergence to lead it towards a compensation mechanism for the static and slow aberrations. References are calculated using an iterative lucky-imaging approach, where each iteration updates the WFS reference, ultimately favoring high-quality focal plane images.Results. We validated this concept through both numerical simulations and on-sky testing on the SCExAO instrument at the 8.2-m Subaru telescope. Simulations show a rapid convergence towards the correction of 82% of the NCPAs. On-sky tests were performed over a 10 minute run in the visible (750 nm). We introduced a flux concentration (FC) metric to quantify the point spread function (PSF) quality and measure a 15.7% improvement compared to the pre-DrWHO image. Conclusions. The DrWHO algorithm is a robust focal-plane wavefront sensing calibration method that has been successfully demonstrated on-sky. It does not rely on a model and does not require wavefront sensor calibration or linearity. It is compatible with different wavefront control methods, and can be further optimized for speed and efficiency. The algorithm is ready to be incorporated in scientific observations, enabling better PSF quality and stability during observations.

Adaptive Optics (AO) is a technology that permits to measure and mitigate the distortion effects of atmospheric turbulence on optical beams. AO must operate in real-time by controlling thousands of actuators to shape the surface of deformable mirrors deployed on ground-based telescopes to compensate for these distortions. The command vectors that trigger how each individual actuator should act to bend a portion of the mirror are obtained from Matrix-Vector Multiplications (MVM). We identify and leverage the data sparsity structure of these control matrices coming from the MAVIS instruments for the European Southern Observatory's Very Large Telescope. We provide performance evaluation on x86 and acceleratorbased systems. We present the impact of tile low-rank (TLR) matrix approximations on time-to-solution for the MVM and assess the produced image quality. We achieve performance improvement up to two orders of magnitude for TLR-MVM compared to regular dense MVM, while maintaining the image quality.

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Accurate astrometry is a key deliverable for the next generation of multi-conjugate adaptive optics (MCAO) systems. The MCAO Visible Imager and Spectrograph (MAVIS) is being designed for the Very Large Telescope Adaptive Optics Facility and must achieve 150 μas astrometric precision (50 μas goal). To test this before going on-sky, we have created MAVISIM, a tool to simulate MAVIS images. MAVISIM accounts for three major sources of astrometric error, high- and low-order point spread function (PSF) spatial variability, tip-tilt residual error and static field distortion. When exploring the impact of these three error terms alone, we recover an astrometric accuracy of 50 μas for all stars brighter than m = 19 in a 30s integration using PSF-fitting photometry. We also assess the feasibility of MAVIS detecting an intermediate mass black hole (IMBH) in a Milky Way globular cluster. We use an N-body simulation of an NGC 3201-like cluster with a central 1500 M⊙ IMBH as input to MAVISIM and recover the velocity dispersion profile from proper motion measurements. Under favourable astrometric conditions, the dynamical signature of the IMBH is detected with a precision of ∼0.20 km/s in the inner ∼4″ of the cluster where HST is confusion-limited. This precision is comparable to measurements made by Gaia, HST and MUSE in the outer ∼60″ of the cluster. This study is the first step towards building a science-driven astrometric error budget for an MCAO system and a prediction of what MAVIS could do once on sky. https://github.com/smonty93/MAVISIM

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