Multi-object adaptive optics on-sky results with Raven

Lardière, Olivier; Andersen, D. R.; Blain, Celia; Bradley, Colin; Gamroth, Darryl; Jackson, Kate; Lach, Przemek; Nash, Reston; Venn, Kim A.; Véran, Jean-Pierre; Correia, Carlos; Oya, Shin; Hayano, Yutaka; Terada, Hiroshi; Ono, Yoshito; Akiyama, Masayuki

doi:10.1117/12.2055480

Cited by 21 publications

(30 citation statements)

References 7 publications

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“…RAVEN has also a closedloop SH-WFS in each science optical path for calibration, classical closed-loop Single-Conjugate AO (SCAO) We have successfully completed the three on-sky runs on the Subaru telescope with RAVEN, and the total number of night is 12 nights: The first run in May 13-14, 2014, the second run in August 9-10, 2014, and the third run in und 23-July 1, 2015. The very first result based on the result from the first run has already been reported in Lardière et al (2014). 8 In this paper, we present the statistical on-sky performance of RAVEN using the IRCS images and the WFS telemetries from the all three on-sky runs.…”

Section: Introductionmentioning

confidence: 99%

“…The very first result based on the result from the first run has already been reported in Lardière et al (2014). 8 In this paper, we present the statistical on-sky performance of RAVEN using the IRCS images and the WFS telemetries from the all three on-sky runs. In Section 2, we briefly describe our asterisms used on-sky and focused in this paper.…”

Section: Introductionmentioning

confidence: 99%

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On-sky MOAO performance evaluation of RAVEN

et al. 2016

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This paper presents the AO performance we got on-sky with RAVEN, a Multi-Object Adaptive Optics (MOAO) technical and science demonstrator installed and tested at the Subaru telescope. We report Ensquared-Energy (EE) and Full Width at Half Maximum (FWHM) measured from science images on Subaru's IRCS taken during all of the on-sky observing runs. We show these metrics as function of different AO modes and atmospheric conditions for two asterisms of natural guide stars. The performances of the MOAO and Ground-Layer AO (GLAO) modes are between the classical Single-Conjugate AO (SCAO) and seeing-limited modes. We achieve the EE of 30 % in H-band with the MOAO correction, which is a science requirement for RAVEN. The MOAO provides sightly better performance than the GLAO mode in both asterisms. One of the reasons which cause this small difference between the MOAO and GLAO modes may be the strong GL contribution. Also, the performance of the MOAO modes is affected by the accuracy of the on-sky turbulence profiling by the SLOpe Detection And Ranging (SLODAR) method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On-sky MOAO performance evaluation of RAVEN

et al. 2016

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“…1,2 Although historically in closed-loop, open-loop systems have also been deployed onsky. 3,4 The developments covered here apply to both configurations.…”

Section: Introductionmentioning

confidence: 99%

Advanced control laws for the new generation of AO systems

Correia¹

2018

Adaptive Optics Systems VI

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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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Multi-object adaptive optics on-sky results with Raven

Cited by 21 publications

References 7 publications

On-sky MOAO performance evaluation of RAVEN

On-sky MOAO performance evaluation of RAVEN

Advanced control laws for the new generation of AO systems

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

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