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Applied Physics Letters

Enhanced all-optical vector atomic magnetometer enabled by artificial neural network

Jianan Qin, Jinxin Xu, Zhiyuan Jiang, Jifeng Qu

Published Sep 2, 2024• 3 citations
Publisher

Abstract

This paper reports an all-optical vector magnetometer enhanced by a machine learning model. Using a dual probing beam setup, spin projections in both probe directions are simultaneously detected. Vector information is directly obtained from the measured phases of spin projection signals. To enhance the measurement accuracy and mitigate the dead zone effect, we introduce an artificial neural network (ANN) to link the phase signals to the field direction. With the addition of amplitude information to the ANN input, the average angle error is reduced to less than 0.3° within a hemisphere. Furthermore, this configuration demonstrates a field angle sensitivity of better than 30 μ rad/Hz1/2.

DOI
10.1063/5.0218065

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•Nov 17, 2020•0 citations
This paper reports an all-optical vector magnetometer enhanced by a machine learning model. Using a dual probing beam setup, spin projections in both probe directions are simultaneously detected. Vector information is directly obtained from the measured phases of spin projection signals. To enhance the measurement accuracy and mitigate the dead zone effect, we introduce an artificial neural network (ANN) to link the phase signals to the field direction. With the addition of amplitude information to the ANN input, the average angle error is reduced to less than 0.3° within a hemisphere. Furthermore, this configuration demonstrates a field angle sensitivity of better than 30 μ rad/Hz1/2.
This paper reports an all-optical vectorial operation method of a Bell-Bloom magnetometer. The spin projections in x and y axes are detected simultaneously by the dual probing beams configuration. And the vector information can be extracted from the phases of spin projection signals. By introducing an artificial neural network (ANN) to establish the relationship between signals and field direction, the phase errors are corrected automatically. With the assistance of ANN, the accuracy of the system has been greatly improved and dead zones have been eliminated basically.
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 fT/√Hz and angular sensitivities of about 100∼200μrad/√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.
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 [Formula: see text] and angular sensitivities of about [Formula: see text] (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.
Quantum magnetometry provides remarkable sensitivity and miniaturization potential for precision measurement, yet conventional vector magnetic field detection often suffers from hardware complexity and directional dead zones. Here we present a deep learning-based, single-beam all-optical vector magnetometer that enables end-to-end neural network modeling for omnidirectional magnetic field sensing without dead zones. By exploiting the nonlinear relationship between coherent population trapping (CPT) spectra of 87Rb D1 line and field orientation, we construct a one-dimensional deep residual network to directly decode single-pass spectral signals into azimuth angles, fully resolving the φ/φ+180° ambiguity inherent in traditional approaches. The experimental results demonstrate that the proposed scheme has a good effect on eliminating degeneracy problems, and the system performance is tested that pitch angular sensitivity of 0.01°/Hz1/2, and azimuth angular sensitivity of 0.01rad without additional modulation coils or multi-beam configurations. This work establishes a general framework for intelligent spectral inversion of complex physical quantities and offers a single-beam architecture suitable for highly integrated quantum sensors.
We report an all-optical atomic vector magnetometer using dual Bell-Bloom optical pumping beams in a Rb vapor cell. This vector magnetometer consists of two orthogonal optical pumping beams, with amplitude modulations at 85Rb^{85}\mathrm{Rb}85Rb and 87Rb^{87}\mathrm{Rb}87Rb Larmor frequencies, respectively. We simultaneously detect atomic signals excited by these two pumping beams using a single probe beam in the third direction, and extract the field orientation information using the phase delays between the modulated atomic signals and the driving beams. By adding a Herriott cavity inside the vapor cell, we improve the magnetometer sensitivity. We study the performance of this vector magnetometer in a magnetic field ranging from 100 mG to 500 mG, and demonstrate a field angle sensitivity better than 10 $\ensuremath{\mu}\mathrm{rad}/{\mathrm{Hz}}^{1/2}$ above 10 Hz.
Vector field measurement is demonstrated with an all-optical scalar atomic magnetometer using intrinsic parameters related to its scalar operation. The Bell-Bloom type atomic magnetometer measures the Larmor precession of cesium atoms through on-resonant absorption of a probe beam. While the AC component of the probe signal is used for the field magnitude measurement, the probe DC signal contains information about the polar angle, defined as the angle between the magnetic field and the probe beam. Additional polar angle information is obtained from the light-shift-induced magnetic field caused by the frequency modulation of the probe beam. With a measurement time of 100 milliseconds, better than 0.02 degree sensitivity has been achieved using a commercial miniaturized sensor at the optimal sensor orientation. The angle measurement accuracy is checked against an optical encoder over the entire polar angle range of 0 to 180 degrees. Better than 1 degree error is recorded over most set polar angles. Azimuthal angle measurement is also exhibited with an additional orthogonally oriented sensor.