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arXiv:1807.03619 · Physical Review Letters

Self-Calibrating Vector Atomic Magnetometry through Microwave Polarization Reconstruction

Tobias Thiele, Yiheng Lin, Mark O. Brown, Cindy A. Regal

Published Oct 8, 2018• 36 citations
arXivHTMLPDF

Abstract

Atomic magnetometry is one of the most sensitive ways to measure magnetic fields. We present a method for converting a naturally scalar atomic magnetometer into a vector magnetometer by exploiting the polarization dependence of hyperfine transitions in rubidium atoms. First, we fully determine the polarization ellipse of an applied microwave field using a self-calibrating method, i.e., a method in which the light-atom interaction provides everything required to know the field in an orthogonal laboratory frame. We then measure the direction of an applied static field using the polarization ellipse as a three-dimensional reference defined by Maxwell's equations. Although demonstrated with trapped atoms, this technique could be applied to atomic vapors, or a variety of atomlike systems.

DOI
10.1103/physrevlett.121.153202

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Atomic magnetometry is one of the most sensitive ways to measure magnetic fields. We present a method for converting a naturally scalar atomic magnetometer into a vector magnetometer by exploiting the polarization dependence of hyperfine transitions in rubidium atoms. First, we fully determine the polarization ellipse of an applied microwave field using a self-calibrating method, i.e., a method in which the light-atom interaction provides everything required to know the field in an orthogonal laboratory frame. We then measure the direction of an applied static field using the polarization ellipse as a three-dimensional reference defined by Maxwell's equations. Although demonstrated with trapped atoms, this technique could be applied to atomic vapors, or a variety of atomlike systems.
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.
This paper reports on a fast, nondestructive and sensitive measurement technique that uses atomic spin precession and non-destructive Faraday rotation probing to measure all three components of the magnetic field with kHz bandwidth. In contrast to other vector magnetometer techniques that modulate an applied magnetic field, here the initial atomic polarization is modulated and the magnetic field components are extracted from the resulting free-induction decay (FID) signal, monitored via paramagnetic Faraday rotation. An ensemble of up to 106cold87Rb in the F = 1 hyperfine ground state was used and held in an optical trap as the field sensor.
Robust calibration of vector optically pumped magnetometers (OPMs) is a nontrivial task, but increasingly important for applications requiring high-accuracy such as magnetic navigation, geophysics research, and space exploration. Here, we showcase a vector OPM that utilizes Rabi oscillations driven between the hyperfine manifolds of 87 Rb to measure the direction of a DC magnetic field against the polarization ellipse structure of a microwave field. By relying solely on atomic measurements—free-induction decay (FID) signals and Rabi measurements across multiple atomic transitions—this sensor can detect drift in the microwave vector reference and compensate for systematic shifts caused by off-resonant driving, nonlinear Zeeman (NLZ) effects, and buffer gas collisions. To facilitate deadzone-free operation, we also introduce a Rabi measurement that utilizes dressed-state resonances that appear during simultaneous Larmor precession and Rabi driving (SPaR). These measurements, performed within a microfabricated vapor cell platform, achieve an average vector accuracy of 0.46 mrad and vector sensitivities down to 11µrad/Hz for geomagnetic field strengths near 50 µT. This performance surpasses the challenging 1-deg (17 mrad) accuracy threshold of several contemporary OPM methods utilizing atomic vapors with an electromagnetic vector reference.
Quantum optical sensing is a maturing field that offers the capability to achieve extraor- dinary sensitivity. Atomic magnetometers, for example, now have the capability to detect brain signals and to precisely measure Earth’s gravity field. In optical atomic magnetome- ters, the electronic spins of the atoms in vapor form are aligned in the same direction by “pumping” the ensemble using laser light at a select frequency. Any magnetic field alters the spin states, which changes the optical properties of the vapor, such as the transmission of the laser light, providing a way to measure magnetic fields. After reviewing the theory of atomic structure and atom-light interactions relevant to the research presented here, I describe in the first part of this thesis a vector magnetometry method using microwave-assisted optical pumping, in which a microwave field resonant to the ground state hyperfine splitting of 87Rb pumps an ensemble of warm rubidium vapor, in addition to a strong optical pump beam, and a weaker optical probe beam. I present theoretical details as well as preliminary data demonstrating magnetic field measurements of a DC magnetic field. The measurements were taken when the DC field was aligned at various angles relative to the microwave magnetic field alignment. I compare theoretical and experimental differences in the measurements. With new methods for quantum devices comes the need for portability. In the second part of this thesis, we demonstrate the fabrication of millimeter-sized rubidium vapor cells using silicon micro-machining techniques. This method is relatively inexpensive compared to glass-blowing methods and allows rapid, large-scale production. I describe our process flow and demonstrate the presence of rubidium atoms inside our vapor cells. To conclude, suggestions for improvement are discussed.
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.