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Adaptive Optics (AO) systems rely on atmospheric turbulence models in order to reduce the effect of wave-front aberrations on image quality. Due to the nature of turbulence, these models can exploit shift-invariant structures without a severe loss in generality. The resulting subset of possible state-matrices is efficiently characterised for identification using Quadratic Programming (QP). Additionally, the initial assumption of shift-invariance is relaxed in order to accommodate for the boundary effect of finite-pupils.

Adaptive Optics (AO) improves the image quality of ground-based telescopes, by compensating in real-time for the blurring effects of atmospheric turbulence. AO systems of future Extremely Large Telescopes (ELTs) will have to operate at much bigger scales (in terms of degrees of freedom) and faster control rates (in terms of loop frequency) to realise their full potential. We have investigated and simulated an AO control methodology to stream pixels "as they come" using a rolling-shutter sCMOS camera to reconstruct 2D images. Compared to a traditional global shutter implementation, the initial results indicate the rolling shutter can reduce control loop latency by a factor of two to four, using existing hardware. This means we can detect twice the number of photons while sampling twice as slowly. Nevertheless, significant technical challenges remain in implementing the rolling functionality, especially when integrating off- The -shelf software and hardware, which is often constricted by a closed-source code base. Furthermore, as the rolling shutter readout is asynchronous, questions remain about coupling and aliasing of telescope vibrations into the imaging system, causing distortions of time and space. If successfully implemented in practice, the rolling shutter approach has the potential to allow astronomers and engineers to capture better scientific observations closer to the diffraction limit.

We report on the conceptual design study done for the Ground Layer Adaptive Optics system of the ULTIMATE-Subaru project. This is an ambitious instrument project, providing GLAO correction in a square field of view of 14 arcmin on a side, aiming to deliver improved seeing at the near infrared wavelength. Its client instruments are an imager and multi-IFU spectrograph at Cassegrain and a Multi-Object spectrograph at Nasmyth. In this paper, we introduce the ULTIMATE-Subaru project overview and its science case and report the results of the GLAO performance prediction based on the numerical simulation and conceptual design of the wavefront sensor system

As space debris in lower Earth orbits are accumulating, techniques to lower the risk of space debris collisions must be developed. Within the context of the Space Environment Research Centre (SERC), the Australian National University (ANU) is developing an adaptive optics system for tracking and pushing space debris. The strategy is to pre-condition a laser launched from a 1.8 m telescope operated by Electro Optics Systems (EOS) on Mount Stromlo, Canberra and direct it at an object to perturb its orbit. Current progress towards implementing this experiment, which will ensure automated operation between the telescope and the adaptive optics system, will be presented.

Space debris in low Earth orbit (LEO) below 1500 km is becoming an increasing threat to spacecrafts. To manage the threat, we are developing systems to improve the ground-based tracking and imaging of space debris and satellites. We also intend to demonstrate that it is possible to launch a high-power laser that modifies the orbits of the debris. However, atmospheric turbulence makes it necessary to use adaptive optics with such systems. When engaging with objects in LEO, the objects are available only a limited amount of time. During the observation window, the object has to be acquired and performance of all adaptive optics feedback loops optimised. We have implemented a high-level adaptive optics supervision tool to automatise time-consuming tasks related to calibration and performance monitoring. This paper describes in detail the current features of our software.

We report on the design and initial laboratory testing of the Adaptive Optics Imaging (AOI) system. AOI has been developed by the Research School of Astronomy and Astrophysics (RSAA) at the Australian National University (ANU), in partnership with the Space Environment Research Centre (SERC), for imaging satellites and debris in low Earth orbit (LEO) and geostationary orbit (GEO). From AO corrected images we will resolve features greater than 50 cm allowing size, shape and orientation characterisation.

Non-Common Path Errors (NCPEs) are the dominant factor limiting the performance of current astronomical high-contrast imaging instruments. If uncorrected, the resulting quasi-static speckle noise floor limits coronagraph performance to a raw contrast of typically 10−4, a value which does not improve with increasing integration time. The coronagraphic Modal Wavefront Sensor (cMWS) is a hybrid phase optic which uses holographic PSF copies to supply focal-plane wavefront sensing information directly from the science camera, whilst maintaining a bias-free coronagraphic PSF. This concept has already been successfully implemented on-sky at the William Herschel Telescope (WHT), La Palma, demonstrating both real-time wavefront sensing capability and successful extraction of slowly varying wavefront errors under a dominant and rapidly changing atmospheric speckle foreground. In this work we present an overview of the development of the cMWS and recent first light results obtained using the Leiden EXoplanet Instrument (LEXI), a high-contrast imager and high-dispersion spectrograph pathfinder instrument for the WHT.

We investigate the potential of phase-diversity (PD) and Gerchberg-Saxton (GS) algorithms in the calibration of active instruments. A set of images is recorded with the focal-plane scientific camera, each image having a known and unique defocus. The phase-retrieval algorithms are used, with those images, to estimate the non-common path aberration that needs to be compensated by correct alignment of the instrument. We demonstrate by numerical simulations that such algorithms, in particular GS, are sufficient detection methods to fully correct wavefronts with an rms error at least up to 6 rad - but this requires several iterative correction stages.

We show experimental results demonstrating the feasibility of an extremely fast sequential phase-diversity (SPD) algorithm for point sources. The algorithm can be implemented on a typical adaptive optics (AO) system to improve the wavefront reconstruction beyond the capabilities of a wavefront sensor by using the information from the imaging camera. The algorithm is based on a small-phase approximation enabling fast numerical implementation, and it finds the optimal wavefront correction by iteratively updating the deformable mirror. Our experiments were made at an AO-setup with a 37 actuator membrane mirror, and the results show that the algorithm finds an optimal image quality in 5-10 iterations, when the initial wavefront errors are typical non-common path aberrations having a magnitude of 1-1.5 rad rms. The results are in excellent agreement with corresponding numerical simulations.

We present a promising approach to the extremely fast sensing and correction of small wavefront errors in adaptive optics systems. As our algorithm's computational complexity is roughly proportional to the number of actuators, it is particularly suitable to systems with 10,000 to 100,000 actuators. Our approach is based on sequential phase diversity and simple relations between the point-spread function and the wavefront error in the case of small aberrations. The particular choice of phase diversity, introduced by the deformable mirror itself, minimizes the wavefront error as well as the computational complexity. The method is well suited for high-contrast astronomical imaging of point sources such as the direct detection and characterization of exoplanets around stars, and it works even in the presence of a coronagraph that suppresses the diffraction pattern. The accompanying paper in these proceedings by Korkiakoski et al. describes the performance of the algorithm using numerical simulations and laboratory tests.

We propose a new approach for the joint estimation of aberration parameters and unknown object from diversity images with applications in imaging systems with extended objects as astronomical ground-based observations or solar telescopes. The motivation behind our idea is to decrease the computational complexity of the conventional phase diversity (PD) algorithm and avoid the convergence to local minima due to the use of nonlinear estimation algorithms. Our approach is able to give a good starting point for an iterative algorithm or it can be used as a new wavefront estimation method. When the wavefront aberrations are small, the wavefront can be approximated with a linear term which leads to a quadratic point-spread function (PSF) in the aberration parameters. The presented approach involves recording two or more diversity images and, based on the before mentioned approximation estimates the aberration parameters and the object by solving a system of bilinear equations, which is obtained by subtracting from each diversity image the focal plane image. Moreover, using the quadratic PSFs gives improved performance to the conventional PD algorithm through the fact that the gradients of the PSFs have simple analytical formulas.

In this paper we give a new wavefront estimation technique that overcomes the main disadvantages of the phase diversity (PD) algorithms, namely the large computational complexity and the fact that the solutions can get stuck in a local minima. Our approach gives a good starting point for an iterative algorithm based on solving a linear system, but it can also be used as a new wavefront estimation method. The method is based on the Born approximation of the wavefront for small phase aberrations which leads to a quadratic point-spread function (PSF), and it requires two diversity images. First we take the differences between the focal plane image and each of the two diversity images, and then we eliminate the constant object, element-wise, from the two equations. The result is an overdetermined set of linear equations for which we give three solutions using linear least squares (LS), truncated total least squares (TTLS) and bounded data uncertainty (BDU). The last two approaches are suited when considering measurements affected by noise. Simulation results show that the estimation is faster than conventional PD algorithms.