Performance Modeling for the RAVEN Multi-Object Adaptive Optics Demonstrator

Andersen, David R.; Jackson, Kate; Blain, Celia; Bradley, Colin; Correia, Carlos; Ito, Meguru; Lardière, Olivier; Véran, Jean-Pierre

doi:10.1086/665924

Cited by 28 publications

(22 citation statements)

References 39 publications

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“…This phenomenon is linked to the variation of the optical disturbances across the field and is characterized by the anisoplanatic angle, θ 0 ∝ λ 6∕5 , typically of the order of tens of arcseconds in the H band [1].…”

Section: Multi-object Adaptive Opticsmentioning

confidence: 99%

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Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems

et al. 2013

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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.

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Section: Multi-object Adaptive Opticsmentioning

confidence: 99%

“…(12) is further explored in Section 4.A for the Raven demonstrator [1]. When phase is expanded onto the Zernike polynomials, the SA cross-covariance functions can be analytically computed for the infinite outer-scale case of turbulence [14,15]:…”

Section: A Equivalent Tomographic Reconstructorsmentioning

confidence: 99%

Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems

et al. 2013

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“…The Raven error budget and performance modeling estimates are presented in detail elsewhere 10 . In summary, by choosing WFSs with 10x10 subapertures and 0.4 arcseconds/pixel and DMs with 11x11 actuators in the pupil, we showed that while the bestcase AO performance of Raven is lowered, performance will be limited by tomographic and WFS noise errors due to the low density of bight NGSs for most Raven science cases.…”

Section: Modelingmentioning

confidence: 99%

“…In these simulations, we were only concerned with the wavefront error (WFE) due to WFS spot sampling and centroiding method. For faint stars, we assumed an integration time of 5.5 ms (180 Hz sampling frequency), so some additional WFEs due to lag will be incurred 10 . The Raven WFSs will accept light between 600 and 900 nm so we also assume a flat spectrum (V-R=0 color) for the NGS to calculate the total photon counts.…”

Section: Modelingmentioning

confidence: 99%

Status of the Raven MOAO science demonstrator

et al. 2012

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Raven is a Multi-Object Adaptive Optics (MOAO) scientific demonstrator which will be used on-sky at the Subaru observatory. Raven is currently being built at the University of Victoria AO Lab. In this paper, we present an overview of the final Raven design and then describe lab tests involving prototypes of Raven subsystems. The final design includes three open loop wavefront sensors (WFSs), a laser guide star WFS and two figure/truth WFSs. Two science channels, each containing a deformable mirror (DM), feed light to the Subaru IRCS spectrograph. Central to the Raven MOAO system is a Calibration Unit (CU) which contains multiple sources, a telescope simulator including two rotating phase screens and a ground layer DM that can be used to calibrate and test Raven. We are working with the Raven CU and open loop WFSs to test and validate our open loop calibration and alignment techniques.

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“…Multi-object adaptive optics (MOAO) is one of these techniques [10,11]. CARMEN is a MOAO reconstructor created at the University of Oviedo.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

González-Gutiérrez

Santos

Martínez‐Zarzuela

et al. 2017

Sensors

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Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

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Performance Modeling for the RAVEN Multi-Object Adaptive Optics Demonstrator

Cited by 28 publications

References 39 publications

Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems

Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems

Status of the Raven MOAO science demonstrator

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

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