Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics
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-bas...
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| Language: | English |
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University of Baghdad
2024-03-01
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| Series: | Iraqi Journal of Physics |
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| Online Access: | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/1203 |
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| author | Raaid Nawfee Hassan |
| author_facet | Raaid Nawfee Hassan |
| author_sort | Raaid Nawfee Hassan |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-d266465cbc6b4f76af41b52dcb41c02d |
| institution | Kabale University |
| issn | 2070-4003 2664-5548 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Iraqi Journal of Physics |
| spelling | doaj-art-d266465cbc6b4f76af41b52dcb41c02d2024-12-23T08:27:22ZengUniversity of BaghdadIraqi Journal of Physics2070-40032664-55482024-03-0122110.30723/ijp.v22i1.1203Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive OpticsRaaid Nawfee Hassan0https://orcid.org/0000-0003-4172-8500Department of Astronomy and Space, College of Science, University of Baghdad. Baghdad, Iraq 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. https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/1203Adaptive OpticsOptical TurbulenceNeural NetworkMLPStrehl Ratio |
| spellingShingle | Raaid Nawfee Hassan Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics Iraqi Journal of Physics Adaptive Optics Optical Turbulence Neural Network MLP Strehl Ratio |
| title | Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics |
| title_full | Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics |
| title_fullStr | Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics |
| title_full_unstemmed | Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics |
| title_short | Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics |
| title_sort | compensation of atmospheric turbulence phase distortion using neural network adaptive optics |
| topic | Adaptive Optics Optical Turbulence Neural Network MLP Strehl Ratio |
| url | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/1203 |
| work_keys_str_mv | AT raaidnawfeehassan compensationofatmosphericturbulencephasedistortionusingneuralnetworkadaptiveoptics |