Dedicated tip-tilt loops are commonly implemented on adaptive optics (AO) systems. In addition, a number of recent high-performance systems feature tip-tilt controllers which are more efficient than the integral action controller. In this context, Linear Quadratic Gaussian (LQG) tip-tilt regulators based on stochastic models identified from AO telemetry have demonstrated their capacity to effectively compensate for the cumulated effects of atmospheric disturbance, windshake and vibrations. These tip-tilt LQG regulators can also be periodically re-tuned during AO operations, thus allowing to track changes in the disturbances’ temporal dynamics. This paper investigates the potential benefit of extending the number of low-order modes to be controlled using models identified from AO telemetry. The global stochastic dynamical model of a chosen number of turbulent low-order modes is identified through data-driven modelling from wavefront sensor measurements. The remaining higher modes are modelled using priors with autoregressive models of order 2. The loop is then globally controlled using the optimal LQG regulator build from all these models. Our control strategy allows for combining a dedicated tip-tilt loop with a deformable mirror that corrects for the remaining low-order modes and for the higher orders altogether, without resorting to mode decoupling. Performance results are obtained through evaluation of the Strehl ratio computed on H-band images from the scientific camera, or in replay mode using on-sky AO telemetry recorded in July 2019 on the CANARY instrument.

Ground-based adaptive optics (AO) systems can use temporal control techniques to greatly improve image resolution. A measure of wind velocity as a function of altitude is needed to minimize the temporal errors associated with these systems. Spatio-temporal analysis of AO telemetry can express the wind velocity profile using the SLODAR technique. However, the limited altitude-resolution of current AO systems makes it difficult to disentangle the movement of independent layers. It is therefore a challenge to create an algorithm that can recover the wind velocity profile through SLODAR data analysis. In this study we introduce a novel technique for automated wind velocity profiling from AO telemetry. Simulated and on-sky centroid data from CANARY - an AO testbed on the 4.2 m William Herschel telescope, La Palma - is used to demonstrate the proficiency of the technique. Wind velocity profiles measured on-sky are compared to contemporaneous measurements from Stereo-SCIDAR, a dedicated high-resolution atmospheric profiler. They are also compared to European centre for medium-range weather forecasts. The software package that we developed to complete this study is open source.

No abstract

No abstract

The use of artificial Intelligence techniques has become widespread in many fields of science, due to their ability to learn from real data and adjust to complex models with ease. These techniques have landed in the field of adaptive optics, and are being used to correct distortions caused by atmospheric turbulence in astronomical images obtained by ground-based telescopes. Advances for multi-object adaptive optics are considered here, focusing particularly on artificial neural networks, which have shown great performance and robustness when compared with other artificial intelligence techniques. The use of artificial neural networks has evolved to the extent of the creation of a reconstruction technique that is capable of estimating the wavefront of light after being deformed by the atmosphere. Based on this idea, different solutions have been proposed in recent years, including the use of new types of artificial neural networks. The results of techniques based on artificial neural networks have led to further applications in the field of adaptive optics, which are included in here, such as the development of new techniques for solar observation or their application in novel types of sensors.