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Register a new userMachine learning based localization and classification with atomic magnetometers
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We demonstrate identification of position, material, orientation and shape of objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97$%$ are obtained. This circumvents the need of solving the inverse problem, and demonstrates the extension of machine learning to diffusive systems such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security.
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Electromagnetic induction imaging with atomic magnetometers has disclosed unprecedented domains for imaging, from security screening to material characterization. However, applications to low-conductivity specimens -- most notably for biomedical imaging -- require sensitivity, stability, and tunability only speculated thus far. Here, we demonstrate contactless and non-invasive imaging down to 50 S/m using a 50 fT/Hz$^{-1/2}$ $^{87}$Rb radio-frequency atomic magnetometer operating in an unshielded environment and near room temperature. Two-dimensional images of test objects are obtained with a near-resonant imaging approach, which reduces the phase noise by a factor 172, with projected sensitivity of 1 S/m. Our results, an improvement of more than three orders of magnitude on previous imaging demonstrations, push electromagnetic imaging with atomic magnetometers to regions of interest for semiconductors, insulators, and biological tissues.
Atomic comagnetometers, which measure the spin precession frequencies of overlapped species simultaneously, are widely applied to search for exotic spin-dependent interactions. Here we propose and implement an all-optical single-species Cs atomic comagnetometer based on the optical free induction decay (FID) signal of Cs atoms in hyperfine levels $F_g=3~&~4$ within the same atomic ensemble. We experimentally show that systematic errors induced by magnetic field gradients and laser fields are highly suppressed in the comagnetometer, but those induced by asynchronous optical pumping and drift of residual magnetic field in the shield dominate the uncertainty of the comagnetometer. With this comagnetometer system, we set the constraint on the strength of spin-gravity coupling of the proton at a level of $10^{-18}$ eV, comparable to the most stringent one. With further optimization in magnetic field stabilization and spin polarization, the systematic errors can be effectively suppressed, and signal-to-noise ratio (SNR) can be improved, promising to set more stringent constraints on spin-gravity interactions.
We present a self-calibrating, SI-traceable broadband Rydberg-atom-based radio-frequency (RF) electric field probe (the Rydberg Field Probe or RFP) and measurement instrument (Rydberg Field Measurement System or RFMS). The RFMS comprises an atomic RF field probe (RFP), connected by a ruggedized fiber-optic patch cord to a portable mainframe control unit with a software interface for RF measurement and analysis including real-time field readout and RF waveform visualization. The instrument employs electromagnetically induced transparency (EIT) readout of spectral signatures from RF-sensitive Rydberg states of an atomic vapor for continuous, pulsed, and modulated RF field measurement. The RFP exploits resonant and off-resonant Rydberg-field interactions to realize broadband RF measurements at frequencies ranging from ~10 MHz to sub-THz over a wide dynamic range. The RFMS incorporates an RF-field-free atomic reference and a laser-frequency tracker to ensure reliability and accuracy of the RF measurement. We characterize the RFP and measure polar field and polarization patterns of the RFP at 12.6 GHz RF in the far-field of a standard gain horn antenna. Measurements at 2.5 GHz are also performed. Measured patterns are in good agreement with simulations. A detailed calibration procedure and uncertainty analysis are presented that account for deviations from an isotropic response over a $4pi$ solid angle, arising from dielectric structures external to the atomic measurement volume. Contributions to the measurement uncertainty from the fundamental atomic measurement method and associated analysis as well as material, geometry, and hardware design choices are accounted for. A calibration (C) factor is used to establish absolute-standard SI-traceable calibration of the RFP. Pulsed and modulated RF field measurement, and time-domain RF-pulse waveform imaging are also demonstrated.
We describe our research programme on the use of atomic magnetometers to detect conductive objects via electromagnetic induction. The extreme sensitivity of atomic magnetometers at low frequencies, up to seven orders of magnitude higher than a coil-based system, permits deep penetration through different media and barriers, and in various operative environments. This eliminates the limitations usually associated with electromagnetic detection.
Solid state battery technology is motivated by the desire to deliver flexible power storage in a safe and efficient manner. The increasingly widespread use of batteries from mass-production facilities highlights the need for a rapid and sensitive diagnostic for identifying battery defects. We demonstrate the use of atomic magnetometry to measure the magnetic fields around miniature solid-state battery cells. These fields encode information about battery manufacturing defects, state of charge, impurities, or can provide important insights into ageing processes. Compared with SQUID-based magnetometry, the availability of atomic magnetometers, however, highlights the possibility for a low-cost, portable, and flexible implementation of battery quality-control and characterization technology.
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