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Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer
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 configu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142828/ https://www.ncbi.nlm.nih.gov/pubmed/37112348 http://dx.doi.org/10.3390/s23084007 |
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author | Dawson, Rach O’Dwyer, Carolyn Irwin, Edward Mrozowski, Marcin S. Hunter, Dominic Ingleby, Stuart Riis, Erling Griffin, Paul F. |
author_facet | Dawson, Rach O’Dwyer, Carolyn Irwin, Edward Mrozowski, Marcin S. Hunter, Dominic Ingleby, Stuart Riis, Erling Griffin, Paul F. |
author_sort | Dawson, Rach |
collection | PubMed |
description | 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 ([Formula: see text]), 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 [Formula: see text] to [Formula: see text]. 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. |
format | Online Article Text |
id | pubmed-10142828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101428282023-04-29 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. Sensors (Basel) Article 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 ([Formula: see text]), 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 [Formula: see text] to [Formula: see text]. 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. MDPI 2023-04-15 /pmc/articles/PMC10142828/ /pubmed/37112348 http://dx.doi.org/10.3390/s23084007 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dawson, Rach O’Dwyer, Carolyn Irwin, Edward Mrozowski, Marcin S. Hunter, Dominic Ingleby, Stuart Riis, Erling Griffin, Paul F. Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title | Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title_full | Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title_fullStr | Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title_full_unstemmed | Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title_short | Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer |
title_sort | automated machine learning strategies for multi-parameter optimisation of a caesium-based portable zero-field magnetometer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142828/ https://www.ncbi.nlm.nih.gov/pubmed/37112348 http://dx.doi.org/10.3390/s23084007 |
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