Nondestructive characterization of laser-cooled atoms using machine learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908569092227072 |
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| author | De Sousa, G. Doris, M. D'Amato, D. Egleston, B. Zwolak, J. P. Spielman, I. B. |
| author_facet | De Sousa, G. Doris, M. D'Amato, D. Egleston, B. Zwolak, J. P. Spielman, I. B. |
| contents | We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluorescence -- that is intrinsic to the cooling process. In-situ snap-shot images of fluorescing atomic ensembles directly reveal the spatial structure of these millimeter-scale objects but contain no obvious information regarding internal properties such as the temperature. We first assembled and labeled a balanced dataset sampling $8\times10^3$ different experimental parameters that includes examples with: large and dense atomic ensembles, a complete absence of atoms, and everything in between. We describe a range of models trained to predict atom number and temperature solely from fluorescence images. These run the gamut from a poorly performing linear regression model based only on integrated fluorescence to deep neural networks that give number and temperature with fractional uncertainties of $0.1$ and $0.2$ respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26479 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nondestructive characterization of laser-cooled atoms using machine learning De Sousa, G. Doris, M. D'Amato, D. Egleston, B. Zwolak, J. P. Spielman, I. B. Atomic Physics Quantum Physics We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluorescence -- that is intrinsic to the cooling process. In-situ snap-shot images of fluorescing atomic ensembles directly reveal the spatial structure of these millimeter-scale objects but contain no obvious information regarding internal properties such as the temperature. We first assembled and labeled a balanced dataset sampling $8\times10^3$ different experimental parameters that includes examples with: large and dense atomic ensembles, a complete absence of atoms, and everything in between. We describe a range of models trained to predict atom number and temperature solely from fluorescence images. These run the gamut from a poorly performing linear regression model based only on integrated fluorescence to deep neural networks that give number and temperature with fractional uncertainties of $0.1$ and $0.2$ respectively. |
| title | Nondestructive characterization of laser-cooled atoms using machine learning |
| topic | Atomic Physics Quantum Physics |
| url | https://arxiv.org/abs/2509.26479 |