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NIST - National Institute of Standards and Technology
05/13/2026 | Press release | Distributed by Public on 05/14/2026 03:25
Nondestructive characterization of laser-cooled atoms using machine learning
Published
May 13, 2026
Author(s)
Ian Spielman, Justyna Zwolak, Michael Doris, Dario D'Amato, Guilherme de Sousa, Brady Egleston
Abstract
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 range 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.
Citation
Physical Review Applied
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Journals
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Citation
Spielman, I. , Zwolak, J. , Doris, M. , D'Amato, D. , de Sousa, G. and Egleston, B. (2026), Nondestructive characterization of laser-cooled atoms using machine learning, Physical Review Applied, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960803 (Accessed May 14, 2026)
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NIST - National Institute of Standards and Technology published this content on May 13, 2026, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on May 14, 2026 at 09:25 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]