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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Nondestructive characterization of laser-cooled atoms using machine learning
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, , , , 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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Created May 13, 2026