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Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (root mean square error, mean structural similarity and Strehl ratio). The reconstructed wavefronts from both techniques are compared for wavefronts of 153 Zernike modes. For this case, a detailed comparison and grid search to find the most suitable neural network is performed, searching between multi-layer perceptron, CNN and recurrent networks topologies. In general, the best network was a CNN trained for TPI-WFS reconstruction, achieving better performance than the reconstruction software from TPI-WFS in most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values.

It is well known that Adaptive Optics (AO) observations do require the availability of natural stars (beside laser guide stars - LGSs) to obtain the tip-tilt and focus information of the atmospheric turbulence. Bright natural stars are not always available imposing the ultimate limit to the AO technology due to the small technical field planned for 40m range telescopes. The use of multiple LGSs, with their respective wavefront sensors and tomographic computations, requires the proper reconstruction of the turbulence column adding significant complexity and cost to the Adaptive Optics systems. Precise knowledge of the tip-tilt information is extremely useful for the accurate pointing of lasers in groundto-space optical communications and in space situational awareness applications. In these contexts the tip-tilt information cannot be obtained from natural stars. Our group have proposed an alternative way of measuring all relevant values of the atmospheric turbulence, specifically including tip-tilt, focus and also higher order aberrations, and tomographic information based on the use of the foreseen density anisotropies in the sodium layer. Results of an analysis using the available information about sodium layer profiles will be presented, showing up to what point anisotropies with the proper spatial and time scales could be expected. The requirements of a laser launch system capable of illuminating uniformly the metapupil at the height of the Na layer are identified and special attention is paid to the required laser power. The extended object generated at the Na-Layer should then be analysed with a wavefront sensor suited to that characteristic. The plenoptic camera is the potential candidate under study in this paper.

Mathematical modelling methods have several limitations when addressing complex physics whose calculations require considerable amount of time. This is the case of adaptive optics, a series of techniques used to process and improve the resolution of astronomical images acquired from ground-based telescopes due to the aberrations introduced by the atmosphere. Usually, with adaptive optics the wavefront is measured with sensors and then reconstructed and corrected by means of a deformable mirror. An improvement in the reconstruction of the wavefront is presented in this work, using convolutional neural networks (CNN) for data obtained from the Tomographic Pupil Image Wavefront Sensor (TPI-WFS). The TPI-WFS is a modified curvature sensor, designed for measuring atmospheric turbulences with defocused wavefront images. CNNs are well-known techniques for its capacity to model and predict complex systems. The results obtained from the presented reconstructor, named Convolutional Neural Networks in Defocused Pupil Images (CRONOS), are compared with the results of Wave-Front Reconstruction (WFR) software, initially developed for the TPI-WFS measurements, based on the least-squares fit. The performance of both reconstruction techniques is tested for 153 Zernike modes and with simulated noise. In general, CRONOS showed better performance than the reconstruction from WFR in most of the turbulent profiles, with significant improvements found for the most turbulent profiles, overall, obtaining around 7% of improvements in wavefront restoration, and 18% of improvements in Strehl.

The Gran Telescopio Canarias Adaptive Optics (GTCAO) will measure the wavefront with a Shack-Hartmann sensor. This wavefront sensor (WFS) is based on the CCD220, an electron-multiplying CCD (EMCCD) that achieves sub-electron readout noise, increasing the signal to noise ratio when weak natural guide stars (NGS) are used as reference. GTCAO will start its operation in telescope with NGS, using only one wavefront sensor, and later it will incorporate a Laser Guide Star (LGS) and consequently a second WFS, also based on an EMCCD. Both EMCCDs and a third one used as spare, have been characterized and compared including the system gain, electron- multiplication gain, readout noise vs gain, excess noise and linearity. The EM gain calibration is important to keep all EMCCD channels in the linear regime and the camera manufacturer carries it out, but it is reported that the multiplication gain may suffer ageing and degradation even if the camera is not in use. This suggests the need to monitor this ageing. In this paper it is proposed and tested a procedure for predictive maintenance that re-characterize the system gain, electron- multiplication gain and linearity periodically in order to predict the eventual ageing of the EMCCD multiplying registers. This procedure can be carried out quickly while the detector is installed in the WFS and in operational status. In order to provide the required illumination, the GTCAO calibration system is used.

The Sun is an ideal object for the development of Multi-Conjugate Adaptive Optics (MCAO) since solar structure provides multiple “guide stars” in any desired configuration. Current MCAO systems imply a number of wavefront sensors, each of them sensing an area of the Sun. The plenoptic camera is a novel concept for wavefront sensing, capable of measuring the tomography of the atmospheric turbulence. By using it, the possibility of a MCAO system with a single sensor becomes a reality. This paper describes the functionality of the plenoptic camera as a tomographic wavefront sensor after implementing it into the OOMAO simulator. Simulation results demonstrate the potential of this sensor to reconstruct atmospheric turbulence in different layers by using a synthetic solar image as a reference.

The Uplink Wavefront Corrector System (UWCS) has been conceived by following the premises from simulation outcomes as a pathfinder instrument to demonstrate Adaptive Optics uplink correction in both applications: Free-Space Optical Communications and self-propagating path of Laser Guide Stars. Preliminary design and expected performance have been analysed via simulations in previous studies. Current activities focus on the turbulence measurement on the launch axis itself by using pulsed or modulated lasers and a pulse-synchronized Shack-Hartmann as wavefront sensor. Present paper describes the test bench it has been built in the Instituto de Astrofisica de Canarias (IAC) to experimentally demonstrate the uplink correction and its advantages.

Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work are presented computational results from a modified curvature sensor, the Tomographic Pupil Image Wave-front Sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials.

The Optical Ground Station (OGS), installed in the Teide Observatory since 1995, was built as part of ESA efforts in the research field of satellite optical communications to test laser telecommunication terminals on board of satellites in Low Earth Orbit and Geostationary Orbit. As far as one side of the link is settled on the Earth, the laser beam (either on the uplink or on the downlink) has to bear with the atmospheric turbulence. Within the framework of designing an Adaptive Optics system to improve the performance of the Free-Space Optical Communications at the OGS, turbulence conditions regarding uplink and downlink have been simulated within the OOMAO (Object-Oriented Matlab Adaptive Optics) Toolbox as well as the possible utilization of a Laser Guide Star to measure the wavefront in this context. Simulations have been carried out by reducing available atmospheric profiles regarding both night-time and day-time measurements and by having into account possible seasonal changes. An AO proposal to reduce atmospheric aberrations and, therefore, ameliorate FSO links performance is presented and analysed in this paper

The use of AO in Extremely Large Telescopes, used to improve performances in smaller telescopes, becomes now mandatory to achieve diffraction limited images according to the large apertures. On the other hand, the new dimensions push the specifications of the AO systems to new frontiers where the order of magnitude in terms of computation power, time response and the required numbers of actuators impose new challenges to the technology. In some aspects implementation methods used in the past result no longer applicable. This paper examines the real dimension of the problem imposed by ELTs and shows the results obtained in the laboratory for a real modal wavefront recovery algorithm (Hudgin) implemented in FPGAs. Some approximations are studied and the performances in terms of configuration parameters are compared. Also a preferred configuration will be justified.