MOSAIC is a mixed-mode multiple object spectrograph planned for the ELT that uses a tiled focal plane to support a variety of observing modes. The MOSAIC AO system uses 4 LGS WFS and up to 4 NGS WFS positioned anywhere within the full 10 arcminute ELT field of view to control either the ELT M4/5 alone for GLAO operation feeding up to 200 targets in the focal plane, or M4/5 in conjunction with 10 open-loop DMs for MOAO correction. In this paper we present the overall design and performance of the MOSAIC GLAO and MOAO systems.
One of the most useful techniques in astronomical instrumentation is image slicing. It enables a spectrograph to have a more compact angular slit, whilst retaining throughput and increasing resolving power. Astrophotonic components like the photonic lanterns and photonic reformatters can be used to replace bulk optics used so far. This study investigates the performance of such devices using end-to-end simulations to approximate realistic on-sky conditions. It investigates existing components, tries to optimize their performance and aims to understand better how best to design instruments to maximize their performance. This work complements the recent work in the field and provides an estimation for the performance of the new components.
With the next-generation of Extremely Large Telescopes (ELTs), the demands of adaptive optics real-time control (AO RTC) increase massively compared to the most complex AO systems in use today. Green Flash, an ongoing EU funded project, is investigating the optimal architecture for ELT scale AO RTC, with an emphasis on GPU and many core CPU solutions. The Intel Xeon Phi range of x86 CPUs is our current focus of investigation into CPU technologies to solve the ELT-scale AO RTC problem. Built using Intels Many Integrated Core (MIC) architecture incorporating 64 general purpose x86 CPU cores into a single CPU package paired with a large pool of on-chip high bandwidth MCDRAM, the Xeon Phi includes many of the advantages of current technologies. The current generation Xeon Phi is readily compatible with standard Linux operating systems and all of the tools and libraries, and as a standard socketed CPU it eliminates the latency introduced by the extra data transfers required for previous Xeon Phis and other accelerator devices. The Durham Adaptive Optics Real-time Controller (DARC) is a freely available, on-sky tested, fully modular, x86 CPU based AO RTC which which is ideally suited to be a basis for our investigation into ELT scale AO RTC performance. We present a proof of concept AO RTC system, in collaboration with the Green Flash project, for ELT scale MCAO, with the requirements of the MAORY AO system in mind, using an optimised DARC on Xeon Phi hardware to achieve the required performance.
Six Laser Guide Stars (LGS) are included in the design of the European Extremely Large Telescope (ELT), with all of its current instruments taking advantage of them using Shack-Hartmann (SH) wavefront sensors (WFS). However, this implementation raises new issues related to the unprecedented elongation that results from the perspective effect combined to the thickness of the sodium layer. In order to investigate wavefront sensing with an elongated LGS on a SH WFS, we are taking advantage of the presence of the multi-object adaptive optics demonstrator CANARY on the William Herschel Telescope (WHT), in La Palma island, that was upgraded with a sodium LGS WFS for our experiment. The LGS is generated by ESO’s transportable Wendelstein LGS unit and the elongation is obtained by positioning the laser launch telescope 40 meters away from the WHT. With this experiment we are able to measure wavefronts using an elongated LGS WFS. In this paper, we present results obtained during the latest run of observations in September 2017. In these results is comprised an error breakdown of wavefront measurement on elongated LGS. The performances of several centroiding methods are compared thanks to this error breakdown. Additionally, we take advantage of varying observation conditions with respect to seeing and sodium profile to establish the robustness of the different centroiding methods. Finally, these performances are evaluated for different SH designs, to explore which compromises can be reached with respect to pixel scale and sub-aperture field of view.
The Green Flash initiative responds to a critical challenge in the astronomical community. Scaling up the real-time control solutions of AO instruments in operation to the specifications of the AO modules at the core of the next generation of extremely large telescopes is not a viable option. The main goal of this project is to design and build a prototype for an AO RTC targeting the E-ELT first-light AO instrumentation. We have proposed innovative technical solutions based on emerging technologies in High Performance Computing, assessed this enabling technologies through prototyping and are now assembling a full scale demonstrator to be validated with a simulator and eventually tested on sky. In this paper, we report on downselection process that led us to the final prototype architecture and the performance of our full scale prototype obtained with a real-time simulator.
The optical turbulence profile is a key parameter in tomographic reconstruction. With interest in tomographic adaptive optics for the next generation of ELTs, turbulence profiling campaigns have produced large quantities of data for observing sites around the world. In order to be useful for Monte Carlo AO simulation, these large datasets must be reduced to a small number of profiles. There is commonly large variation in the structure of the turbulence, therefore statistics such as the median and interquartile range of each altitude bin become less representative as features in the profile are averaged out. Here we present the results of the use of a hierarchical clustering method to reduce the 2018A Stereo-SCIDAR dataset from ESO Paranal, consisting of over 10,000 turbulence profiles measured over 83 nights, to a small set of 18 that represent the most commonly observed profiles.
Adaptive Optics (AO) is a necessary technology for ensuring the success of the next generation of extremely large telescopes (ELTs). It’s used to help mitigate the perturbing effects of Earth’s atmosphere on the incoming light from astronomical objects and will be an integral part of ELTs for obtaining close to diffraction limited images. To maintain a correction of the incoming wavefront under dynamic atmospheric conditions, which can change significantly on the order of milliseconds, the frame-by-frame reconstruction must be operated in real-time, with hard limits on the time interval between measuring the disturbance and applying a correction. The main problem size for AO RTC increases with the 4th power of telescope diameter and so the computational demands of AO RTCs for ELTs, with primary mirror diameters between 20-40m, increase significantly compared to the current generation of 10m class telescopes. This makes the investigation into and the development of real-time controllers (RTCs) for ELT scale AO systems critical for ensuring the effectiveness of these instruments for first light. Green Flash, which is an ongoing EU funded project, has the aim of investigating the optimal hardware architecture for ELT scale AO RTC, with an emphasis on GPU and Xeon Phi solutions. The Intel Xeon Phi, built using Intel’s Many Integrated Core (MIC) architecture, incorporates ≥64 general purpose x86 CPU cores into a single CPU package paired with a large pool of on chip high bandwidth MCDRAM, it has many of the advantages of current technologies without some of the more significant drawbacks. The most computationally intensive aspects of most AO RTC pipelines are large matrix-vector multiplications mainly used to compute the reconstructed wavefronts which are highly parallelizable and are generally memory bandwidth bound. This makes the Xeon Phi with it’s large CPU count and high bandwidth memory ideally suited for acceleration of the reconstruction task and therefore for ELT scale AO RTC. The most recent incarnation of the Xeon Phi platform is available as a standard socketed x86 CPU allowing previous efforts made in developing CPU based RTC software to be used as a basis for a Xeon Phi based RTCs with the added advantage that any optimisations made for the MIC architecture can be carried forward to future x86 CPU based systems. The Durham Adaptive Optics Real-time Controller (DARC) is an example of a freely available, on-sky tested, fully modular, x86 CPU based AO RTC which which is ideally suited to be a basis for our investigation into ELT scale AO RTC performance. We present a proof of concept AO RTC system, in collaboration with the Green Flash project, using an optimised DARC on a multi-node homogeneous Xeon Phi cluster to demonstrate the potential of the MIC platform for AO RTC. We will present our methods of optimisation for the C based DARC for the Xeon Phi, including BIOS, kernel and OS tuning as well as considerations for multi-threading and massively parallel algorithm development.
CANARY is a wide-field AO on-sky test facility which has been operated annually on the 4.2m William Herschel Telescope since 2010. CANARY has the stated goal of testing and demonstrating AO technologies that are critical for ELT AO performance. It has seen four distinct phases where new AO technologies have been developed and demonstrated, including NGS MOAO in 2010 (phase A), Rayleigh LGS and NGS MOAO in 2012 and 2013 (phase B, with LGS commissioning in 2011), LTAO operation in 2014 and 2015, and finally operation with a single Sodium laser guide star launched far off axis in 2016 and 2017 (phase D). By launching this laser guide star 40m off axis, extremely elongated laser guide star spots are created in the CANARY LGS Shack-Hartmann wavefront sensor. Therefore, the 7×7 sub-apertures of CANARY can be used to test wavefront sensing performance of a sub-pupil of the ELT located furthest from the laser launch axis. We present an overview of CANARY in its phase D configuration. Depending on where in the sky the LGS is pointing, the projected baseline between the on-axis LGS wavefront sensor and the laser launch location, as seen by the wavefront sensor, will vary from about 20-40m, allowing us to artificially generate different degrees of elongation. Additionally, the well sampled CANARY sub-apertures have 30×30 pixels each and a 20 arcsecond field of view, using an OCAM2S EMCCD camera. This means that by shrinking sub-apertures, and optionally by binning pixels, we are able to investigate different pixel scales and fields of view for the ELT systems, thus determining the optimal design parameters. Here we discuss the closed loop tests that were performed to investigate the effect of spot truncation and extreme elongation. We include different correlation techniques, including standard FFT-based correlation, brute force correlation and correlation by difference squared. We also mention dynamic and automatic updates of the correlation reference images while the AO loop is engaged that have previously been reported. The matched filter algorithm is also mentioned, with a pointer to our prior on-sky investigations. We give our recommendation for the ELT wavefront sensing algorithm of choice, and our evidence based reasons for this recommendation, which may come as a surprise to some. Finally we also present the future experiments to be performed with CANARY, give details of the OPTICON funded programme which enables the hosting of AO experiments on CANARY, allowing the AO community to get involved.
The proposed MOSAIC first-generation instrument for the ELT is a multi-object spectrograph utilising a combined MOAO and GLAO system. With 8 separate wavefront sensors (4 LGS and 4 NGS), and 10 separate deformable mirrors, in addition to the ELT M4 mirror, MOSAIC represents one of the most challenging ELT instruments for real-time control, using a total of approximately 65,000 slope measurements to control approximately 26,000 actuators with a 250 Hz LGS frame rate. The proposed modular design of real-time control system to be used with MOSAIC is presented. This is based on the Durham AO Real-time Controller (DARC), and uses 12x Intel Xeon Phi nodes (6U rack space, approx 2.5 kW under load) to obtain the required performance. We describe the prototyping activities performed at Durham, including estimates of AO system latency and jitter. The design challenges are presented, along with the techniques used to overcome these. The full modular architecture is described, including the system interfaces, control and configuration middleware, telemetry subsystem, and the hard real-time core pipeline. One benefit of our design is the ability to simultaneously test different AO control algorithms, which represents a significant opportunity for automatic optimisation of AO system performance. We discuss this concept, and present an artificial neural network solution for machine learning, which can be used to automatically improve MOSAIC performance with time. Algorithms that can be optimised in this way are discussed, include pixel calibration and processing techniques, wavefront slope measurement routines, wavefront reconstruction techniques and associated parameters, and temporal filtering methods, including vibration control. The hardware design for the real-time control system is presented, including an overview of the network architecture, the interconnections between computational nodes, and the method by which all pixels from all 8 wavefront sensors are processed concurrently.
We present the outcomes of an evaluation of middleware technologies for adaptive optics real-time control against the requirements of the Green Flash project, which are derived from the most demanding requirements of proposed first generation E-ELT instruments. The technology down-selection process applied in Green Flash is described, and measured performance of the selected middlewares on the hardware of a Green Flash prototype RTC are presented.
Correlation wavefront sensing for extended objects requires both reference images for cross-correlation and centroiding parameters for measuring the shift in the correlation image. We present a method for optimising centroiding parameters demonstrated on a center of mass measurement of the correlation images. The process can be entirely automated and offloaded out of the adaptive optics system into a separate pipeline and so not interfere with the adaptive optics system. This means the method is implemented entirely in software, so can be added to any existing adaptive optics system which employs correlation wavefront sensors.
To approach optimal performance advanced Adaptive Optics (AO) systems deployed on ground-based telescopes must have accurate knowledge of atmospheric turbulence as a function of altitude. Stereo-SCIDAR is a high-resolution stereoscopic instrument dedicated to this measure. Here, its profiles are directly compared to internal AO telemetry atmospheric profiling techniques for CANARY (Vidal et al. 20141), a Multi-Object AO (MOAO) pathfinder on the William Herschel Telescope (WHT), La Palma. In total twenty datasets are analysed across July and October of 2014. Levenberg-Marquardt fitting algorithms dubbed Direct Fitting and Learn 2 Step (L2S; Martin 20142) are used in the recovery of profile information via covariance matrices - respectively attaining average Pearson product-moment correlation coefficients with stereo-SCIDAR of 0.2 and 0.74. By excluding the measure of covariance between orthogonal Wavefront Sensor (WFS) slopes these results have revised values of 0.65 and 0.2. A data analysis technique that combines L2S and SLODAR is subsequently introduced that achieves a correlation coefficient of 0.76.
This paper presents preliminary daytime profiles taken using a Wide-Field Shack-Hartmann Sensor at the Swedish
Solar Telescope (SST), La Palma. These are contrasted against Stereo-SCIDAR data from corresponding nights to
assess the validity of the assumptions currently used for simulating the performances of possible Multi-Conjugate
Adaptive Optics (MCAO) systems for future solar telescopes, especially the assumption that the structure of the high
altitude turbulence is mostly similar between the day and the night. We find that for our data both the altitude and the
strength of the turbulence differ between the day and the night, although more data is required to draw any conclusions
about typical behaviour and conditions.