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.
Laser Guide Stars (LGS) have greatly increased the sky-coverage of Adaptive Optics (AO) systems. Due to the up-link turbulence experienced by LGSs, a Natural Guide Star (NGS) is still required, limiting sky-coverage. A method has recently been presented that promises to determine the LGS uplink tip-tilt in tomographic LGS AO systems by using the fact that each LGS Wave Front Sensor (WFS) in a tomographic AO system observes the uplink path of other LGSs. Such a technique has the potential to greatly increase the sky-coverage of Multi- Object, Laser Tomographic and Multi-Conjugate AO systems by allowed further off-axis NGS tip-tilt stars to be used for correction. Here we use an approach based on phase gradient covariance matrices to create on-sky capable tomographic reconstructors that account for some tip-tilt from LGS WFSs. We present analysis of open loop wave front sensor data from the CANARY Multi-Object AO demonstrator, providing early validation for the technique.
MOSAIC is the MOAO-assisted multi-object spectrograph of the European ELT (E-ELT) under Phase A study. In order to maximise the ensquared energy, each of its near-infrared MOAO channels has is own deformable mirror to supplement the build-in E-ELT deformable mirror M4. This secondary DM uses a tomographic reconstructor optimized for the direction of the target, that comes as a complement to the M4 global ground layer correction. We had described in previous work a simulation scheme that allows us to assess the performance of a E-ELT scaled MOAO instrument. In this article, we will show how we have modified this previous single DM simulation to the 2-DM case through two different ways of computing the tomographic error. We compare the performances and the computation time of each method. Finally we present the application of our simulation tool to the MOSAIC case.
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 <i>et al.</i> 2014<sup>1</sup>), 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 <i>Direct Fitting </i>and <i>Learn 2 Step</i> (<i>L2S</i>; Martin 2014<sup>2</sup>) 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 <i>L2S </i>and SLODAR is subsequently introduced that achieves a correlation coefficient of 0.76.
MOSAIC is the proposed multiple-object spectrograph for the E-ELT that will utilise the widest possible field of view provided by the telescope. In terms of adaptive optics, there are two distinct operating modes required to meet the top-level science requirements. The MOSAIC High Multiplex Mode (HMM) requires either seeing-limited or GLAO correction within a 0.6 (NIR) and 0.9 (VIS) arcsecond sub-fields over the widest possible field for a few hundred objects. To achieve seeing limited operation whilst maintaining the maximum unvignetted field of view for scientific observation will require recreating some of the functionality present in the Pre-Focal Station relating to control of the E-ELT active optics. MOSAIC High Definition Mode Control (HDM) requires a 25% Ensquared Energy (EE) within 150mas in the H-band element for approximately 10 targets distributed across the full E-ELT field, implying the use of Multiple Object AO (MOAO). Initial studies have shown that to meet the EE requirements whilst maintaining high-sky coverage will require the combination of wavefront signals from both high-order NGS and LGS to provide a tomographic estimate for the correction to be applied to the open-loop MOAO DMs. In this paper we present the current MOSAIC AO design and provide the first performance estimates for the baseline instrument design. We then report on the various trade-offs that will be investigated throughout the course of the Phase A study, such as the requirement to mix NGS and LGS signals tomographically. Finally, we discuss how these will impact the AO architecture, the MOSAIC design and ultimately the scientific performance of this wide-field workhorse instrument at the E-ELT.
CANARY is an on-sky Laser Guide Star (LGS) tomographic AO demonstrator in operation at the 4.2m William Herschel Telescope (WHT) in La Palma. From the early demonstration of open-loop tomography on a single deformable mirror using natural guide stars in 2010, CANARY has been progressively upgraded each year to reach its final goal in July 2015. It is now a two-stage system that mimics the future E-ELT: a GLAO-driven woofer based on 4 laser guide stars delivers a ground-layer compensated field to a figure sensor locked tweeter DM, that achieves the final on-axis tomographic compensation. We present the overall system, the control strategy and an overview of its on-sky performance.
In this article we revisit a subject that has partly already been examined in previous studies: the behavior of tomographic reconstructors in adaptive optics systems, facing to an atmospheric profile (C<sup>2</sup><sub>n</sub>(<i>h</i>)) different from the one they've been optimized for. We develop a new approach for that. The current usual approach is to simulate the performance of the reconstructor when slightly varying the C<sup>2</sup><sub>n</sub>(<i>h</i>) profile around a nominal one, and show how far the deviation may go. This has the disadvantage that, as the parameter space for potential errors on the C<sup>2</sup><sub>n</sub>(<i>h</i>) profile is basically infinite, it is particularly uneasy to span. Our approach consists in deriving a sort of sensitivity function, that we call vertical error distribution (VED), from the knowledge of any tomographic reconstructor. This function can be computed even for non-tomographic reconstructors, ground-layers reconstructors, single-conjugate AO reconstructors, etc. In any case, it allows us to derive the error when applied to a particular C<sup>2</sup><sub>n</sub>(<i>h</i>) profile, have a direct, global visualization of the error variation with layer altitude, for any number at any altitude. This also allows us to understand what a given reconstructor is sensitive to, at what altitudes or altitude range, or explain why some GLAO reconstructors may perform better than optimized MMSE tomographic reconstructors if low-altitude layers pop up. We also discuss the case of ELTs and apply our approach to large scale reconstructors.
Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique for wide-field multi-object spectrographs (MOS). MOAO aims at applying dedicated wavefront corrections to numerous separated tiny patches spread over a large field of view (FOV), limited only by that of the telescope. The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. We have developed a novel hybrid, pseudo-analytical simulation scheme, somewhere in between the end-to- end and purely analytical approaches, that allows us to simulate in detail the tomographic problem as well as noise and aliasing with a high fidelity, and including fitting and bandwidth errors thanks to a Fourier-based code. Our tomographic approach is based on the computation of the minimum mean square error (MMSE) reconstructor, from which we derive numerically the covariance matrix of the tomographic error, including aliasing and propagated noise. We are then able to simulate the point-spread function (PSF) associated to this covariance matrix of the residuals, like in PSF reconstruction algorithms. The advantage of our approach is that we compute the same tomographic reconstructor that would be computed when operating the real instrument, so that our developments open the way for a future on-sky implementation of the tomographic control, plus the joint PSF and performance estimation. The main challenge resides in the computation of the tomographic reconstructor which involves the inversion of a large matrix (typically 40 000 × 40 000 elements). To perform this computation efficiently, we chose an optimized approach based on the use of GPUs as accelerators and using an optimized linear algebra library: MORSE providing a significant speedup against standard CPU oriented libraries such as Intel MKL. Because the covariance matrix is symmetric, several optimization schemes can be envisioned to speedup even further the computation. Optimizing the speed of the reconstructor computation is of major interest not only for the design study of MOAO instruments, but also for future routine operations of the system as the reconstructor has to be updated regularly to cope for atmospheric variability.