Machine learning assisted vector atomic magnetometry

Xin Meng, Youwei Zhang, Xichang Zhang, Shenchao Jin, Tingran Wang, Liang Jiang, Liantuan Xiao, Suotang Jia and Yanhong Xiao ()
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Xin Meng: Fudan University
Youwei Zhang: Fudan University
Xichang Zhang: Fudan University
Shenchao Jin: Fudan University
Tingran Wang: The University of Chicago
Liang Jiang: The University of Chicago
Liantuan Xiao: Shanxi University
Suotang Jia: Shanxi University
Yanhong Xiao: Shanxi University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Multiparameter sensing such as vector magnetometry often involves complex setups due to various external fields needed in explicitly connecting one measured signal to one parameter. Here, we propose a paradigm of indirect encoding for vector atomic magnetometry based on machine learning. We encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals associated with the optical rotation of a laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 $${{{{{{{\rm{fT}}}}}}}}/\sqrt{{{{{{{{\rm{Hz}}}}}}}}}$$ fT / Hz and angular sensitivities of about $$100 \sim 200\,\mu rad/\sqrt{{{{{{{{\rm{Hz}}}}}}}}}$$ 100 ~ 200 μ r a d / Hz (for a magnetic field of around 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing.

Date: 2023
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DOI: 10.1038/s41467-023-41676-x

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