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The Durham adaptive optics (AO) real-time controller was initially a proof of concept design for a generic AO control system. It has since been developed into a modern and powerful central-processing-unit-based real-time control system, capable of using hardware acceleration (including field programmable gate arrays and graphical processing units), based primarily around commercial off-the-shelf hardware. It is powerful enough to be used as the real-time controller for all currently planned 8 m class telescope AO systems. Here we give details of this controller and the concepts behind it, and report on performance, including latency and jitter, which is less than 10 [micro]s for small AO systems. OCIS codes: 010.1080, 110.1080.

The EAGLE instrument for the European Extremely Large Telescope is a multi-object integral field unit spectrograph that uses a multi-object adaptive optics (AO) system for wavefront correction of interesting lines of sight. We present a Monte Carlo AO simulation package that has been used to model the performance of EAGLE, and provide results, including comparisons with an analytical code. These results include an investigation of the performance of compressed reconstructor representations that have the potential to significantly reduce the complexity of a real-time control system when implemented. OCIS codes: 010.1080, 110.1080.

We present a design improvement for a recently proposed type of Shack--Hartmann wavefront sensor that uses a cylindrical (lenticular) lenslet array. The improved sensor design uses optical binning and requires significantly fewer detector pixels than the corresponding conventional or cylindrical Shack--Hartmann sensor, and so detector readout noise causes less signal degradation. Additionally, detector readout time is significantly reduced, which reduces the latency for closed loop systems and data processing requirements. We provide simple analytical noise considerations and Monte Carlo simulations, we show that the optically binned Shack--Hartmann sensor can offer better performance than the conventional counterpart in most practical situations, and our design is particularly suited for use with astronomical adaptive optics systems. OCIS codes: 010.1080, 010.7350.

Adaptive optics systems are essential on all large telescopes for which image quality is important. These are complex systems with many design parameters requiring optimization before good performance can be achieved. The simulation of adaptive optics systems is therefore necessary to categorize the expected performance. We describe an adaptive optics simulation platform, developed at Durham University, which can be used to simulate adaptive optics systems on the largest proposed future extremely large telescopes as well as on current systems. This platform is modular, object oriented, and has the benefit of hardware application acceleration that can be used to improve the simulation performance, essential for ensuring that the run time of a given simulation is acceptable. The simulation platform described here can be highly parallelized using parallelization techniques suited for adaptive optics simulation, while still offering the user complete control while the simulation is running. The results from the simulation of a ground layer adaptive optics system are provided as an example to demonstrate the flexibility of this simulation platform. OCIS codes: 010.1080, 010.7350.

A technique used to accelerate an adaptive optics simulation platform using reconfigurable logic is described. The performance of parts of this simulation has been improved by up to 600 times (reducing computation times by this factor) by implementing algorithms within hardware and enables adaptive optics simulations to be carried out in a reasonable time scale. This demonstrates that it is possible to use reconfigurable logic to accelerate computational codes by very large factors when compared with conventional software approaches, and this has relevance for many computationally intensive applications. The use of reconfigurable logic for high performance computing is currently in its infancy and has never before been applied to this field. OCIS codes: 010.1080, 010.7350, 100.2000.

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 that 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 retuned 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 2019 July on the CANARY instrument. [ABSTRACT FROM AUTHOR]

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 slope detection and ranging (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. [ABSTRACT FROM AUTHOR]

We propose a many-core CPU architecture for Extremely Large Telescope (ELT) scale adaptive optics (AO) real-time control (RTC) for the multiconjugate AO (MCAO) and laser-tomographic AO (LTAO) modes. MCAO and LTAO differ from the more conventional single-conjugate (SCAO) mode by requiring more wavefront sensor (WFS) measurements and more deformable mirrors to achieve a wider field of correction, further increasing the computational requirements of ELT-scale AO. We demonstrate results of our CPU-based AO RTC operating firstly in SCAO mode, using either Shack–Hartmann or Pyramid style WFS processing, and then in MCAO mode and in LTAO mode using the specifications of the proposed ELT instruments, MAORY and HARMONI. All results are gathered using a CPU-based camera simulator utilizing UDP packets to better demonstrate the pixel streaming and pipelining of the RTC software. We demonstrate the effects of switching parameters, streaming telemetry and implicit pseudo-open loop control (POLC) computation on the MCAO and LTAO modes. We achieve results of |${\lt }600\, \mu {\rm s}$| latency with an ELT scale SCAO set-up using Shack–Hartman processing and |${\lt }800\, \mu {\rm s}$| latency with SCAO Pyramid WFS processing. We show that our MCAO and LTAO many core CPU architecture can achieve full system latencies of |${\lt }1000\, \mu {\rm s}$| with jitters |${\lt }40\, \mu {\rm s}$| RMS. We find that a CPU-based AO RTC architecture has a good combination of performance, flexibility and maintainability for ELT-scale AO systems. [ABSTRACT FROM AUTHOR]

Advanced adaptive optics (AO) instruments on ground-based telescopes require accurate knowledge of the atmospheric turbulence strength as a function of altitude. This information assists point spread function reconstruction, AO temporal control techniques and is required by wide-field AO systems to optimize the reconstruction of an observed wavefront. The variability of the atmosphere makes it important to have a measure of the optical turbulence profile in real time. This measurement can be performed by fitting an analytically generated covariance matrix to the cross-covariance of Shack–Hartmann wavefront sensor (SHWFS) centroids. In this study we explore the benefits of reducing cross-covariance data points to a covariance map region of interest (ROI). A technique for using the covariance map ROI to measure and compensate for SHWFS misalignments is also introduced. We compare the accuracy of covariance matrix and map ROI optical turbulence profiling using both simulated and on-sky data from CANARY, an AO demonstrator on the 4.2 m William Herschel telescope, La Palma. On-sky CANARY results are compared to contemporaneous profiles from Stereo-SCIDAR  – a dedicated high-resolution optical turbulence profiler. It is shown that the covariance map ROI optimizes the accuracy of AO telemetry optical turbulence profiling. In addition, we show that the covariance map ROI reduces the fitting time for an extremely large telescope-scale system by a factor of 72. The software package we developed to collect all of the presented results is now open source. [ABSTRACT FROM AUTHOR]

We propose a solution to the increased computational demands of Extremely Large Telescope (ELT) scale adaptive optics (AO) real-time control with the Intel Xeon Phi Knights Landing (KNL) Many Integrated Core (MIC) Architecture. The computational demands of an AO real-time controller (RTC) scale with the fourth power of telescope diameter and so the next generation ELTs require orders of magnitude more processing power for the RTC pipeline than existing systems. The Xeon Phi contains a large number (≥64) of low-power x86 CPU cores and high-bandwidth memory integrated into a single socketed server CPU package. The increased parallelism and memory bandwidth are crucial to providing the performance for reconstructing wavefronts with the required precision for ELT scale AO. Here, we demonstrate that the Xeon Phi KNL is capable of performing ELT scale single conjugate AO real-time control computation at over 1.0 kHz with less than 20 $$\mu$$  s RMS jitter. We have also shown that with a wavefront sensor camera attached the KNL can process the real-time control loop at up to 966 Hz, the maximum frame-rate of the camera, with jitter remaining below 20  $$\mu$$  s RMS. Future studies will involve exploring the use of a cluster of Xeon Phis for the real-time control of the MCAO and MOAO regimes of AO. We find that the Xeon Phi is highly suitable for ELT AO real time control. [ABSTRACT FROM AUTHOR]

The next generation of Extremely Large Telescopes (ELTs) for astronomy will rely heavily on the performance of their adaptive optics (AO) systems. Real-time control is at the heart of the critical technologies that will enable telescopes to deliver the best possible science and will require a very significant extrapolation from current AO hardware existing for 4-10 m telescopes. Investigating novel real-time computing architectures and testing their eligibility against anticipated challenges is one of the main priorities of technology development for the ELTs. This paper investigates the suitability of the Intel Xeon Phi, which is a commercial off-the-shelf hardware accelerator. We focus on wavefront reconstruction performance, implementing a straightforward matrix-vector multiplication (MVM) algorithm. We present benchmarking results of the Xeon Phi on a real-time Linux platform, both as a standalone processor and integrated into an existing real-time controller (RTC). Performance of single and multiple Xeon Phis are investigated. We show that this technology has the potential of greatly reducing the mean latency and variations in execution time (jitter) of large AO systems. We present both a detailed performance analysis of the Xeon Phi for a typical E-ELT first-light instrument along with a more general approach that enables us to extend to any AO system size. We show that systematic and detailed performance analysis is an essential part of testing novel real-time control hardware to guarantee optimal science results. [ABSTRACT FROM AUTHOR]

High-fidelity Monte Carlo simulation of atmospheric turbulence phase screens is important for performance testing of astronomical adaptive optics systems.With a sparse spectrummodel and an optimal sampling method, it is possible to generate an atmospheric turbulence phase screen with high fidelity. However, the phase screen generation speed is limited by the algorithm structure of this technique. A non-uniform fast Fourier transform technique is proposed in this paper to accelerate phase screen generation speed. This method is able to generate huge atmospheric turbulence phase screens with high fidelity and an acceptable time-cost enabling practical adaptive optics simulations of forthcoming Extremely Large Telescopes. [ABSTRACT FROM AUTHOR]

CuReD (Cumulative Reconstructor with domain Decomposition) and HWR (Hierarchical Wavefront Reconstructor) are novel wavefront reconstruction algorithms for the Shack-Hartmann wavefront sensor, used in the single-conjugate adaptive optics. For a high-order system they are much faster than the traditional matrix-vector-multiplication method. We have developed three methods for mapping the reconstructed phase into the deformable mirror actuator commands and have tested both reconstructors with the CANARY instrument. We find out that the CuReD reconstructor runs stably only if the feedback loop is operated as a leaky integrator, whereas HWR runs stably with the conventional integrator control. Using the CANARY telescope simulator we find that the Strehl ratio (SR) obtained with CuReD is slightly higher than that of the traditional least-squares estimator (LSE). We demonstrate that this is because the CuReD algorithm has a smoothing effect on the output wavefront. The SR of HWR is slightly lower than that of LSE. We have tested both reconstructors extensively on-sky. They perform well and CuReD achieves agreement between the two. [ABSTRACT FROM AUTHOR]

As the pupil size of ground-based telescopes increases, adaptive optics systems become more and more important for almost all astronomical observations. To test the performance of adaptive optics systems through simulation and with test bench experiments, we need to model the atmospheric turbulence with high fidelity. To fulfil this goal, a new method based on optimal sampling with a sparse spectrum model is discussed. This method can effectively simulate the atmospheric phase screen of any power spectrum with a controllable error budget. [ABSTRACT FROM AUTHOR]