NIST · ИТ · f61e95e90cb0032b19f5
Nondestructive characterization of laser-cooled atoms using machine learning
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
NIST · ИТОфициальный источник · 2026-05-13
{
"title_en": "Nondestructive characterization of laser-cooled atoms using machine learning",
"designation": "",
"series": "",
"status": "",
"date": "2026-05-13",
"date_original": "May 13, 2026",
"url": "https://www.nist.gov/publications/nondestructive-characterization-laser-cooled-atoms-using-machine-learning",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Ian Spielman , Justyna Zwolak , Michael Doris , Dario D'Amato , Guilherme de Sousa, Brady Egleston",
"abstract_excerpt_en": "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",
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"sources": [
"NIST Publications"
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"source_records": [
{
"title_en": "Nondestructive characterization of laser-cooled atoms using machine learning",
"designation": "",
"series": "",
"status": "",
"date": "2026-05-13",
"date_original": "May 13, 2026",
"url": "https://www.nist.gov/publications/nondestructive-characterization-laser-cooled-atoms-using-machine-learning",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Ian Spielman , Justyna Zwolak , Michael Doris , Dario D'Amato , Guilherme de Sousa, Brady Egleston",
"abstract_excerpt_en": "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"
}
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}