Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer

Dawson, Rach; O’Dwyer, Carolyn; Irwin, Edward; Mrozowski, Marcin S.; Hunter, Dominic; Ingleby, Stuart; Riis, Erling; Griffin, Paul F.

doi:10.3390/s23084007

Open AccessArticle

Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer

by

Rach Dawson

^*

,

Carolyn O’Dwyer

^*

,

Edward Irwin

,

Marcin S. Mrozowski

,

Dominic Hunter

,

Stuart Ingleby

,

Erling Riis

and

Paul F. Griffin

Department of Physics, Scottish Universities Physics Alliance SUPA, University of Strathclyde, Glasgow G4 0NG, UK

^*

Authors to whom correspondence should be addressed.

Sensors 2023, 23(8), 4007; https://doi.org/10.3390/s23084007

Submission received: 27 February 2023 / Revised: 11 April 2023 / Accepted: 13 April 2023 / Published: 15 April 2023

(This article belongs to the Special Issue Towards a New Era of MEG Imaging: New Technologies and Clinical Applications)

Download

Browse Figures

Versions Notes

Abstract

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (

T / \sqrt{Hz}

), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM’s operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500

fT / \sqrt{Hz}

to

< 109 fT / \sqrt{Hz}

. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies.

Keywords: magnetometry; atomic; optimisation; machine learning; SERF; caesium

Share and Cite

MDPI and ACS Style

Dawson, R.; O’Dwyer, C.; Irwin, E.; Mrozowski, M.S.; Hunter, D.; Ingleby, S.; Riis, E.; Griffin, P.F. Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer. Sensors 2023, 23, 4007. https://doi.org/10.3390/s23084007

AMA Style

Dawson R, O’Dwyer C, Irwin E, Mrozowski MS, Hunter D, Ingleby S, Riis E, Griffin PF. Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer. Sensors. 2023; 23(8):4007. https://doi.org/10.3390/s23084007

Chicago/Turabian Style

Dawson, Rach, Carolyn O’Dwyer, Edward Irwin, Marcin S. Mrozowski, Dominic Hunter, Stuart Ingleby, Erling Riis, and Paul F. Griffin. 2023. "Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer" Sensors 23, no. 8: 4007. https://doi.org/10.3390/s23084007

APA Style

Dawson, R., O’Dwyer, C., Irwin, E., Mrozowski, M. S., Hunter, D., Ingleby, S., Riis, E., & Griffin, P. F. (2023). Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer. Sensors, 23(8), 4007. https://doi.org/10.3390/s23084007

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI