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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.11793 |
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| _version_ | 1866915344757555200 |
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| author | Ma, Xiaoxiao Liang, Changwen Sha, Rong Zhou, Chao Li, Qixue Wang, Guochao Liu, Jixun Yan, Shuhua Yang, Jun Zhu, Lingxiao |
| author_facet | Ma, Xiaoxiao Liang, Changwen Sha, Rong Zhou, Chao Li, Qixue Wang, Guochao Liu, Jixun Yan, Shuhua Yang, Jun Zhu, Lingxiao |
| contents | Laser cooling, which cools atomic and molecular gases to near absolute zero, is the crucial initial step for nearly all atomic gas experiments. However, fast achievement of numerous sub-$μ$K cold atoms is challenging. To resolve the issue, we propose and experimentally validate an intelligent polarization gradient cooling approach enhanced by optical lattice, utilizing Maximum Hypersphere Compensation Sampling Bayesian Optimization (MHCS-BO). MHCS-BO demonstrates a twofold increase in optimization efficiency and superior prediction accuracy compared to conventional Bayesian optimization. Finally, approximate $10^8$ cold atoms at a temperature of 0.4$\pm$0.2 $μ$K can be achieved given the optimal parameters within 15 minutes. Our work provides an intelligent protocol, which can be generalized to other high-dimension parameter optimization problems, and paves way for preparation of ultracold atom in quantum experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerated Bayesian optimization in deep cooling atoms Ma, Xiaoxiao Liang, Changwen Sha, Rong Zhou, Chao Li, Qixue Wang, Guochao Liu, Jixun Yan, Shuhua Yang, Jun Zhu, Lingxiao Atomic Physics Laser cooling, which cools atomic and molecular gases to near absolute zero, is the crucial initial step for nearly all atomic gas experiments. However, fast achievement of numerous sub-$μ$K cold atoms is challenging. To resolve the issue, we propose and experimentally validate an intelligent polarization gradient cooling approach enhanced by optical lattice, utilizing Maximum Hypersphere Compensation Sampling Bayesian Optimization (MHCS-BO). MHCS-BO demonstrates a twofold increase in optimization efficiency and superior prediction accuracy compared to conventional Bayesian optimization. Finally, approximate $10^8$ cold atoms at a temperature of 0.4$\pm$0.2 $μ$K can be achieved given the optimal parameters within 15 minutes. Our work provides an intelligent protocol, which can be generalized to other high-dimension parameter optimization problems, and paves way for preparation of ultracold atom in quantum experiments. |
| title | Accelerated Bayesian optimization in deep cooling atoms |
| topic | Atomic Physics |
| url | https://arxiv.org/abs/2412.11793 |