&lt;title&gt;Artificial intelligence system and optimized modal control for the ADONIS adaptive optics instrument&lt;/title&gt;

Demailly, L.; Gendron, É.; Beuzit, Jean-Luc; Lacombe, F.; Hubin, N.

doi:10.1117/12.176120

Cited by 4 publications

(2 citation statements)

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“…However, these systems encounter limitations when correcting nonlinear aberrations and necessitate intricate models to estimate the wavefront distortions accurately [4][5][6]. Machine learning in AO was investigated as early as the 1990s [7][8][9]. At that time, artificial neural networks (ANN) were considered to be a good alternative for the wavefront sensing of single-aperture and array telescopes in the multiple mirror telescope (MMT) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics

Hassan

2024

IJP

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Adaptive optics revolutionizes telescopic resolution but faces cost, complexity, and calibration hurdles. Neural network adaptive optics (NNAO) offers promise by using neural networks to tailor corrections to telescopes and atmospheric conditions, by passing calibration and sensors. This MATLAB-based study examines NNAO's impact on astronomical image quality, revealing it as a cost-efficient solution that enhances adaptive optics in astronomy. The numerical simulation results were encouraging, with a compensation rate exceeding 50% due to favorable monitoring conditions. The results indicate that the dominant factor affecting image quality is the variance of wavefront aberrations. The Strehl ratio (SR) decreases from an average of 0.548 for a variance of 0.2 to 0.020 for a variance of 0.6, while the mean squared error (MSE) increases from an average of 0.613 to 5.414. However, the effect on peak signal-to-noise ratio (PSNR) is inconclusive. Furthermore, it was found that increasing the number of neurons and training ratio does not significantly impact the results obtained, but it noticeably affects the computational time required.

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Section: Introductionmentioning

confidence: 99%

Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics

Hassan

2024

IJP

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“…The first AO system dedicated to astronomy was the COME-ON/COME-ON-PLUS instrument (Rigaut et al 1991;Rousset et al 1994). This instrument has recently been upgraded to a new version called ADONIS (Hubin et al 1993;; Demailly et al 1994;Beuzit, Demailly, & Gendron 1997) and installed on the ESO 3.6 m telescope at La Silla, Chile. Several overviews of astrophysical results have already been published (Le ´na 1994(Le ´na , 1995Stecklum 1998).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optics Imaging at 1–5 Microns on Large Telescopes: The COMIC Camera for ADONIS

Lacombe

Marco

Geoffray

et al. 1998

PUBL ASTRON SOC PAC

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A new 1-5 mm high-resolution camera dedicated to the ESO adaptive optics system ADONIS has been developed as a collaborative project of Observatoire de Paris-Meudon and Observatoire de Grenoble, under ESO contract. Since this camera has been designed to correctly sample the diffraction, two focal plate scales are available: 36 mas pixel Ϫ1 for the 1-2.5 mm range and 100 mas pixel Ϫ1 for the 3-5 mm range, yielding fields of view of and , respectively. Several broadband and narrowband filters are available 4Љ .5 # 4Љ .512Љ .8 # 12Љ .8 as well as two circular variable filters, allowing low spectral resolution ( -120) imagery between 1.2 and R ∼ 60 4.8 mm. This camera is equipped with a HgCdTe/CCD array detector built by the CEA-LETI-LIR 128 # 128 (Grenoble, France). Among its main characteristics, this detector offers a remarkably high storage capacity (more than 10 6 electrons) with a total system readout noise of ≈1000 electrons rms, making it particularly well suited for long integration time imagery in the 3-5 mm range of the near-infrared domain. The measured dark current is 2000 electrons s Ϫ1 pixel Ϫ1 at the regular operating temperature of 77 K, allowing long exposure times at short wavelengths ( mm), where the performances are readout-noise limited. At longer wavelengths ( mm), l ! 3 l 1 3 the performances are background-noise limited. We have estimated the ADONIS ϩ COMIC imaging performances using a method specially dedicated to high angular resolution cameras.

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Beuzit¹,

Demailly

Gendron

et al. 1997

Experimental Astronomy

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<title>Artificial intelligence system and optimized modal control for the ADONIS adaptive optics instrument</title>

Cited by 4 publications

References 0 publications

Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics

Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics

Adaptive Optics Imaging at 1–5 Microns on Large Telescopes: The COMIC Camera for ADONIS

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