Abstract
Non-linear curvature wave-front sensing (nlCWFS) delivers outstanding sensitivity and high dynamic range by lifting the linearity constraint of standard curvature wave-front sensing and working in the non-linear Fresnel (near-field) regime [Guyon, 2010]. The goals of this paper are twofold: 1) revisit the phase-diversity PD formalism and attempt to use this framework, originally developed for the Fraunhofer (far-field) regime, with nlCWFS signals and 2) develop formulae making explicit use of the Fresnel regime for later use with gradient-based non-linear minimisation methods.

Abstract
Raven is a Multi-Object Adaptive Optics (MOAO) scientific demonstrator which will be used on-sky at the Subaru observatory from 2014. Raven is currently being built and tested at the University of Victoria AO Lab. This paper presents an overview of the optomechanical design and the software architecture of Raven, and gives the current status of this project. Raven includes three open loop wavefront sensors (WFSs), a laser guide star WFS and two figure/truth WFSs. Two science channels containing deformable mirrors (DMs) feed light to the Subaru IRCS spectrograph. Central to the Raven is a Calibration Unit which contains multiple sources, a telescope simulator including two phase screens and a ground layer DM that can be used to calibrate and test Raven in the lab. Preliminary results on calibration and open-loop AO correction using a tomographic reconstructor are presented.

Abstract
Adaptive Optics (AO) systems use a Deformable Mirror (DM) to counter in real-time the nefarious effects of atmospheric turbulence on ground-based telescopes images. This article presents a brief historical overview of AO design, seen as a strongly multi-variable minimum-variance (MVP) disturbance rejection problem associated with a hybrid continuous/ discrete time MV control problem. It is shown that for a wide class of LTI DM and turbulence models, this hybrid MV problem can be transformed into an equivalent discrete-time LQG formulation. A discrete-time stochastic model enables to compute the optimal control in standard reconstructed feedback form and to evaluate performance degradation for simpler suboptimal solutions. An example to tip-tilt DM control for the European Extremely Large Telescope (E-ELT) is presented.

Abstract

Abstract
Computationally efficient wave-front reconstruction techniques for astronomical adaptive-optics (AO) systems have seen great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered much attention, especially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl ratio) and further develop formulae for the anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise, and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of forward models for the Shack-Hartmann wave-front sensor (using sparse and nonsparse representations) and compute point-spread-function raw intensities. We find that for a 32 × 32 single-conjugated AOs system the aliasing propagation coefficient is roughly 60% of the least-squares filters, whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in the H band. For current and next-generation high-contrast imagers, despite better aliasing mitigation, AA Wiener filtering cannot be used as a standalone method and must therefore be used in combination with optical spatial filters deployed before image formation actually takes place. © 2014 Optical Society of America.

Abstract
Object Oriented Mat lab Adaptive Optics (OOMAO) is a Mat lab toolbox dedicated to Adaptive Optics (AO) systems. OOMAO is based on a small set of classes representing the source, atmosphere, telescope, wavefront sensor, Deformable Mirror (DM) and an imager of an AO system. This simple set of classes allows simulating Natural Guide Star (NGS) and Laser Guide Star (LGS) Single Conjugate AO (SCAO) and tomography AO systems on telescopes up to the size of the Extremely Large Telescopes (ELT). The discrete phase screens that make the atmosphere model can be of infinite size, useful for modeling system performance on large time scales. OOMAO comes with its own parametric influence function model to emulate different types of DMs. The cone effect, altitude thickness and intensity profile of LGSs are also reproduced. Both modal and zonal modeling approach are implemented. OOMAO has also an extensive library of theoretical expressions to evaluate the statistical properties of turbulence wavefronts. The main design characteristics of the OOMAO toolbox are object oriented modularity, vectorized code and transparent parallel computing. OOMAO has been used to simulate and to design the Multi Object AO prototype Raven at the Subaru telescope and the Laser Tomography AO system of the Giant Magellan Telescope. In this paper, a Laser Tomography AO system on an ELT is simulated with OOMAO. In the first part, we set up the class parameters and we link the instantiated objects to create the source optical path. Then we build the tomographic reconstructor and write the script for the pseudo-open-loop controller.