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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

On the Development of an Optical Rubidium Vector Atomic Magnetometer

James McKelvy, Irina V. Novikova, Eugeniy E. Mikhailov, Mario A. Maldonado, I. Fan, Yang Li, Ying-Ju Wang, John E. Kitching, Andrey B. Matsko

Published Jul 16, 2023• 2 citations
Publisher

Abstract

The precise measurement of magnetic fields is a fundamental tool of remote sensing. However, accurately measuring the direction of magnetic fields is challenging with atomic magnetometers. In this work, we discuss progress on the development of an all-optical vector atomic magnetometer that uses electromagnetically induced transparency (EIT) on87Rb vapor. The magnitude of the magnetic field is evaluated by measuring the separation of the laser transmission peaks of the Zeeman-resolved EIT spectra, and the direction is measured by evaluating the relative peak contrast using a lightweight machine learning algorithm involving principal component analysis (PCA). We have demonstrated a scalar sensitivity of $ < 10pT/\sqrt {Hz} $ in the 1–100 Hz band and an angular accuracy of < 1°. This approach for vector magnetometry does not require an array of sensors or calibration coils, ultimately setting the groundwork for the design of a new magnetometer that is lighter and more accurate than conventional vector magnetometer configurations.

DOI
10.1109/igarss52108.2023.10283082

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The precise measurement of magnetic fields is a fundamental tool of remote sensing. However, accurately measuring the direction of magnetic fields is challenging with atomic magnetometers. In this work, we discuss progress on the development of an all-optical vector atomic magnetometer that uses electromagnetically induced transparency (EIT) on87Rb vapor. The magnitude of the magnetic field is evaluated by measuring the separation of the laser transmission peaks of the Zeeman-resolved EIT spectra, and the direction is measured by evaluating the relative peak contrast using a lightweight machine learning algorithm involving principal component analysis (PCA). We have demonstrated a scalar sensitivity of <10pT/Hz< 10pT/\sqrt {Hz}<10pT/Hz​ in the 1–100 Hz band and an angular accuracy of < 1°. This approach for vector magnetometry does not require an array of sensors or calibration coils, ultimately setting the groundwork for the design of a new magnetometer that is lighter and more accurate than conventional vector magnetometer configurations.
We present a realization of a magnetic sensor based on electromagnetically induced transparency (EIT) resonances observed in hot Rb vapor using lin∥\parallel∥lin polarized dichromatic light and evaluate scalar and vector capabilities of the sensor for measuring Earth-like magnetic fields. We demonstrate scalar measurement sensitivity of 2 pT/Hz^1/2 in the 1-100~Hz spectral frequency band using a cm^3 Rb vapor cell, significantly improving the performance for such a configuration if compared with earlier measurements of large magnetic fields. By using a single linearly polarized dichromatic optical field, we are also able to determine the direction of the magnetic field with respect to the light propagation direction and polarization, taking advantage of the symmetries of the interaction scheme. We accomplish that by combining the polarization-sensitive transmission measurements and sparse sensing machine learning techniques. A path for further improvement of the sensitivity and elimination of systematic effects, such as heading error, is discussed.
We present a realization of a magnetic sensor based on electromagnetically induced transparency (EIT) resonances observed in hot Rb vapor using lin∥\parallel∥lin polarized dichromatic light and evaluate scalar and vector capabilities of the sensor for measuring Earth-like magnetic fields. We demonstrate scalar measurement sensitivity of 2 pT/Hz^1/2 in the 1-100~Hz spectral frequency band using a cm^3 Rb vapor cell, significantly improving the performance for such a configuration if compared with earlier measurements of large magnetic fields. By using a single linearly polarized dichromatic optical field, we are also able to determine the direction of the magnetic field with respect to the light propagation direction and polarization, taking advantage of the symmetries of the interaction scheme. We accomplish that by combining the polarization-sensitive transmission measurements and sparse sensing machine learning techniques. A path for further improvement of the sensitivity and elimination of systematic effects, such as heading error, is discussed.
We present a realization of a magnetic sensor based on electromagnetically induced transparency (EIT) resonances observed in hot Rb vapor using lin∥\parallel∥lin polarized dichromatic light and evaluate scalar and vector capabilities of the sensor for measuring Earth-like magnetic fields. We demonstrate scalar measurement sensitivity of 2 pT/Hz^1/2 in the 1-100~Hz spectral frequency band using a cm^3 Rb vapor cell, significantly improving the performance for such a configuration if compared with earlier measurements of large magnetic fields. By using a single linearly polarized dichromatic optical field, we are also able to determine the direction of the magnetic field with respect to the light propagation direction and polarization, taking advantage of the symmetries of the interaction scheme. We accomplish that by combining the polarization-sensitive transmission measurements and sparse sensing machine learning techniques. A path for further improvement of the sensitivity and elimination of systematic effects, such as heading error, is discussed.
Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics. However, a practical methodology of accurately recovering the longitudinal angle of the local field through observations of EIT spectra has not been established. In this work, we address this problem of angle determination with an unsupervised machine learning algorithm utilizing nonlinear dimensionality reduction. The proposed algorithm was developed to interface with spectroscopic measurements from an EIT-based atomic rubidium magnetometer and uses kernel principal component analysis (KPCA) as an unsupervised feature extraction tool. The resulting KPCA features allow each EIT spectrum measurement to be represented by a single coordinate in a new reduced dimensional feature space, thereby streamlining the process of angle determination. A supervised support vector regression (SVR) machine was implemented to model the resulting relationship between the KPCA projections and field direction. If the magnetometer is configured so that the azimuthal angle of the field is defined with a polarization lock, the KPCA-SVR algorithm is capable of predicting the longitudinal angle of the local magnetic field within 1 degree of accuracy and the magnitude of the absolute field with a resolution of 70 nT. The combined scalar and angular sensitivity of this method make the KPCA-enabled EIT magnetometer competitive with conventional vector magnetometry methods.
This project demonstrates an optical method for measuring the strength and\ndirection of a magnetic field using electromagnetically induced transparency\n(EIT) in rubidium vapor. In rubidium atoms, the Zeeman effect causes atomic\nstates to shift when an external magnetic field is applied. The magnitude of\nthese shifts, and consequently the strength of the magnetic field, can be measured\nby recording electromagnetically induced transparency resonances with\ntuned laser fields. Furthermore, since the atom's interaction with the laser field\nis dependent on the direction of the magnetic field and the laser polarization,\nwe are able to determine the direction as well as the strength of the magnetic\nfield by analyzing relative intensities of EIT resonances. With a feasible precision\ndown to 10 picoTesla and the possibility of complete field mapping, this\nmagnetometer can be useful for medical, geophysical, and materials testing\napplications.