Deep learning-driven adaptive optics for laser wavefront correction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866911152641933312 |
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| author | Wang, Jikai Burckhard, Sven Ravi, Sonam Smitha Bauer, Dominik Rominger, Volker Nolte, Stefan Flamm, Daniel |
| author_facet | Wang, Jikai Burckhard, Sven Ravi, Sonam Smitha Bauer, Dominik Rominger, Volker Nolte, Stefan Flamm, Daniel |
| contents | {We report on an intensity-only and deep-learning based method for laser beam characterization that allows to predict the underlying optical field within milliseconds. A simple near-field / far-field camera setup enables online control of an adaptive optics to optimize beam quality. The robustness and precision of the method is enhanced by applying the concept of phase diversity based on spiral phase plates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning-driven adaptive optics for laser wavefront correction Wang, Jikai Burckhard, Sven Ravi, Sonam Smitha Bauer, Dominik Rominger, Volker Nolte, Stefan Flamm, Daniel Optics {We report on an intensity-only and deep-learning based method for laser beam characterization that allows to predict the underlying optical field within milliseconds. A simple near-field / far-field camera setup enables online control of an adaptive optics to optimize beam quality. The robustness and precision of the method is enhanced by applying the concept of phase diversity based on spiral phase plates. |
| title | Deep learning-driven adaptive optics for laser wavefront correction |
| topic | Optics |
| url | https://arxiv.org/abs/2509.10662 |