We present the final results from a high sampling rate, multi-month, spectrophotometric reverberation mapping campaign undertaken to obtain either new or improved Hβ reverberation lag measurements for several relatively low-luminosity active galactic nuclei (AGNs). We have reliably measured the time delay between variations in the continuum and Hβ emission line in six local Seyfert 1 galaxies. These measurements are used to calculate the mass of the supermassive black hole at the center of each of these AGNs. We place our results in context to the most current calibration of the broad-line region (BLR) R BLR -L relationship, where our results remove outliers and reduce the scatter at the low-luminosity end of this relationship. We also present velocity-resolved Hβ time-delay measurements for our complete sample, though the clearest velocity-resolved kinematic signatures have already been published.

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Multi-object adaptive optics (MOAO) systems are still in their infancy: their complex optical designs for tomographic, wide-field wavefront sensing, coupled with open-loop (OL) correction, make their calibration a challenge. The correction of a discrete number of specific directions in the field allows for streamlined application of a general class of spatio-angular algorithms, initially proposed in Whiteley et al. [J. Opt. Soc. Am. A15, 2097 (1998)], which is compatible with partial on-line calibration. The recent Learn & Apply algorithm from Vidal et al. [J. Opt. Soc. Am. A27, A253 (2010)] can then be reinterpreted in a broader framework of tomographic algorithms and is shown to be a special case that exploits the particulars of OL and aperture-plane phase conjugation. An extension to embed a temporal prediction step to tackle sky-coverage limitations is discussed. The trade-off between lengthening the camera integration period, therefore increasing system lag error, and the resulting improvement in SNR can be shifted to higher guide-star magnitudes by introducing temporal prediction. The derivation of the optimal predictor and a comparison to suboptimal autoregressive models is provided using temporal structure functions. It is shown using end-to-end simulations of Raven, the MOAO science, and technology demonstrator for the 8 m Subaru telescope that prediction allows by itself the use of 1-magnitude-fainter guide stars.

RAVEN will be a Multi-Object Adaptive Optics (MOAO) technology and science demonstrator on the Subaru telescope. The baseline design calls for three natural guide star (NGS) wavefront sensors (WFS) and two science pickoff arms that will patrol a ∼2′ diameter field of regard (FOR). Sky coverage is an important consideration, because RAVEN is both a technical and science demonstrator. Early-stage simulation of RAVEN's performance is critical in establishing that the key science requirement can be met. That is, 30% of the energy of an unresolved pointspread function (PSF) be ensquared within a 140 mas slit using existing WFS camera and deformable mirror (DM) technology. The system was simulated with two independent modeling tools, MAOS and OOMAO, which were in excellent agreement. It was established that RAVEN will be an order 10 × 10 adaptive optics (AO) system by examining the tradeoffs between performance, sky coverage, and WFS field of view. The 30% ensquared-energy (EE) requirement will be met with three NGSs and will exceed 40% if the Subaru Laser Guide Star (LGS) is used onaxis (assuming median image quality). This is also true for NGSs as faint as m R ¼ 14:5.

Sodium laser guide stars (LGSs) allow, in theory, Adaptive Optics (AO) systems to reach a full sky coverage, but they have their own limitations. The artificial star is elongated due to the sodium layer thickness, and the temporal and spatial variability of the sodium atom density induces changing errors on wavefront measurements, especially with Extremely Large Telescopes (ELTs) for which the LGS elongation is larger. In the framework of the Thirty-Meter-Telescope project (TMT), the AO-Lab of the University of Victoria (UVic) has built an LGS-simulator test bed in order to assess the performance of new centroiding algorithms for LGS Shack-Hartmann wavefront sensors (SH-WFS). The design of the LGS-bench is presented, as well as laboratory SH-WFS images featuring 29x29 radially elongated spots, simulated for a 30-m pupil. The errors induced by the LGS variations, such as focus and spherical aberrations, are characterized and discussed. This bench is not limited to SH-WFS and can serve as an LGS-simulator test bed to any other LGS-AO projects for which sodium layer fluctuations are an issue.

International audienceMulti-object astronomical adaptive optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arcminutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular linear-quadratic-Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [J. Opt. Soc. Am. A 31, 101 (2014)], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wavefronts are never explicitly estimated in the volume, providing considerable computational savings on 10-m-class telescopes and beyond. We find that for Raven, a 10-m-class MOAO system with two science channels, the SA-LQG improves the limiting magnitude by two stellar magnitudes when both the Strehl ratio and the ensquared energy are used as figures of merit. The sky coverage is therefore improved by a factor of similar to 5. (C) 2015 Optical Society of Americ

Prior statistical knowledge of the atmospheric turbulence is essential for designing, optimizing and evaluating tomographic adaptive optics systems. We present the statistics of the vertical profiles of C 2 N and the outer scale at Maunakea estimated using a Slope Detection And Ranging (SLODAR) method from on-sky telemetry taken by RAVEN, which is a MOAO demonstrator in the Subaru telescope. In our SLODAR method, the profiles are estimated by a fit of the theoretical auto-and cross-correlation of measurements from multiple Shark-Haltmann wavefront sensors to the observed correlations via the non-linear Levenberg-Marquardt Algorithm (LMA), and the analytic derivatives of the spatial phase structure function with respect to its parameters for the LMA are also developed. The estimated profile has the median total seeing of 0.460 and large C 2 N fraction of the ground layer of 54.3 %. The C 2 N profile has a good agreement with the result from literatures, except for the ground layer. The median value of the outer scale is 25.5 m and the outer scale is larger at higher altitudes, and these trends of the outer scale are consistent with findings in literatures.

We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Kármán turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor.This is an extension of the authors' previously published work [2] in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behaviour of the previous results under less ideal conditions. Results show that ±100% wind-speed error and ±50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case. Temporal prediction of the atmosphere is a much debated topic. The purpose of prediction is to reduce the error due to servo lag since the turbulence profile changes rapidly (on timescales of a few milliseconds) during the time that it takes to gather sensor information and to compute corrections.Unlike single-conjugated AO systems, in tomographic AO direct access to an estimate of the layered wavefront (WF) is provided at the end of the tomographic step. With turbulence estimated in a discrete number of layers, the frozen-flow approximation can now be called upon with a higher degree of fidelity [1].Temporal prediction is useful for both MultiConjugate AO (MCAO) and Multi-Object AO (MOAO) as a means to increase the sky-coverage [2, 3]. Allowing for greater integration times whilst compensating for the lag error by applying a predictive algorithm enables the system to guide on fainter sources [2]. Provided information on the dynamics of the atmospheric turbulence is available or can be construed, one should be able to obtain a more accurate estimate of the WF at the time a set of commands is applied to the deformable mirror (DM) and therefore improve performance. Lag errors are also considered a serious limitation in high-contrast imaging systems where the broadening of the PSF along * Corresponding author: katjac@uvic.ca the main axis of wind-blown turbulence severely limits contrast at small separations [4].In this letter we discuss alternatives for timeprogressing the atmospheric wave-fronts namely a nearMarkovian model, auto-regressive models and spatial shifting under frozen-flow [2, 5, 6]. The main goal of our work is to provide insight into the accuracy and robustness bounds of such models in view of a contemporary application to the Raven science and technology demonstrator installed on the Subaru Telescope [7].Under the hypothesis that the turbulent atmosphere is a sum of L thin layers located at a discrete number of different altitudes h l , the aperture-plane phase φ(ρ, θ, t) indexed by the bi-dimensional spatial coordinate vectorwhere ϕ l (ρ, t) is the l th -layer wave-front, ω l is the l th layer strength and H θ is a propagation operator in the near-field approximation that relates the aperture-plane ...