Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning
The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/11/11/1064 |
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| author | Kunkun Cheng Shengqian Wang Xuesheng Liu Yuandong Cheng |
| author_facet | Kunkun Cheng Shengqian Wang Xuesheng Liu Yuandong Cheng |
| author_sort | Kunkun Cheng |
| collection | DOAJ |
| description | The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems. |
| format | Article |
| id | doaj-art-76e1f88a655d42fc910b938a440bb616 |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-76e1f88a655d42fc910b938a440bb6162024-11-26T18:18:27ZengMDPI AGPhotonics2304-67322024-11-011111106410.3390/photonics11111064Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer LearningKunkun Cheng0Shengqian Wang1Xuesheng Liu2Yuandong Cheng3National Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaThe resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems.https://www.mdpi.com/2304-6732/11/11/1064far-field informationtransfer learningsegmented mirrorpiston error |
| spellingShingle | Kunkun Cheng Shengqian Wang Xuesheng Liu Yuandong Cheng Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning Photonics far-field information transfer learning segmented mirror piston error |
| title | Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning |
| title_full | Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning |
| title_fullStr | Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning |
| title_full_unstemmed | Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning |
| title_short | Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning |
| title_sort | co phase error detection for segmented mirrors based on far field information and transfer learning |
| topic | far-field information transfer learning segmented mirror piston error |
| url | https://www.mdpi.com/2304-6732/11/11/1064 |
| work_keys_str_mv | AT kunkuncheng cophaseerrordetectionforsegmentedmirrorsbasedonfarfieldinformationandtransferlearning AT shengqianwang cophaseerrordetectionforsegmentedmirrorsbasedonfarfieldinformationandtransferlearning AT xueshengliu cophaseerrordetectionforsegmentedmirrorsbasedonfarfieldinformationandtransferlearning AT yuandongcheng cophaseerrordetectionforsegmentedmirrorsbasedonfarfieldinformationandtransferlearning |