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Machine Learning Based Localization and Classification with Atomic Magnetometers

Deans, C; Griffin, LD; Marmugi, L; Renzoni, F; (2018) Machine Learning Based Localization and Classification with Atomic Magnetometers. Physical Review Letters , 120 (3) , Article 033204. 10.1103/PhysRevLett.120.033204. Green open access

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Abstract

We demonstrate identification of position, material, orientation, and shape of objects imaged by a ⁸⁵Rb 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.

Type: Article
Title: Machine Learning Based Localization and Classification with Atomic Magnetometers
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevLett.120.033204
Publisher version: https://doi.org/10.1103/PhysRevLett.120.033204
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Magneto-optical spectra, Atoms, Machine learning, Optical pumping, Optically detected magnetic resonance, Pump-probe spectroscopy, Interdisciplinary
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10040623
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