Dual-species atomic absorption image reconstruction using deep neural networks
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913993834102784 |
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| author | Lee, Kyuhwan Shin, Yong-il |
| author_facet | Lee, Kyuhwan Shin, Yong-il |
| contents | Optical imaging plays an instrumental role in understanding the behavior of trapped neutral atoms. In this work, we describe a deep learning-based online image completion protocol that reduces interference fringes in optical absorption signals for a dual-species atomic system. Regardless of the distinct nature of the task for two different atomic species, 6Li and 23Na, the method displays a robust solution for suppressing fringes. To incorporate this into daily operations, a transfer learning scheme is required that incrementally updates the previously learned parameters. We outline an online image completion method that efficiently adapts to drifting experimental conditions. Our method can be easily integrated into lab settings, where transfer learning can accelerate image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-species atomic absorption image reconstruction using deep neural networks Lee, Kyuhwan Shin, Yong-il Quantum Gases Optical imaging plays an instrumental role in understanding the behavior of trapped neutral atoms. In this work, we describe a deep learning-based online image completion protocol that reduces interference fringes in optical absorption signals for a dual-species atomic system. Regardless of the distinct nature of the task for two different atomic species, 6Li and 23Na, the method displays a robust solution for suppressing fringes. To incorporate this into daily operations, a transfer learning scheme is required that incrementally updates the previously learned parameters. We outline an online image completion method that efficiently adapts to drifting experimental conditions. Our method can be easily integrated into lab settings, where transfer learning can accelerate image analysis. |
| title | Dual-species atomic absorption image reconstruction using deep neural networks |
| topic | Quantum Gases |
| url | https://arxiv.org/abs/2508.12120 |