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

Rach Dawson1, Carolyn O'Dwyer1, Edward Irwin1

  • 1Department of Physics, Scottish Universities Physics Alliance SUPA, University of Strathclyde, Glasgow G4 0NG, UK.

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Summary
This summary is machine-generated.

Machine learning (ML) efficiently optimized a caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). This advanced method improved OPM sensitivity from 500 fT/Hz to under 109 fT/Hz, enabling better sensor performance.

Keywords:
SERFatomiccaesiummachine learningmagnetometryoptimisation

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Area of Science:

  • Physics
  • Engineering
  • Computer Science

Background:

  • Machine learning (ML) offers efficient parameter optimization for complex systems.
  • Traditional methods are impractical for high-dimensional parameter spaces.
  • Optically pumped magnetometers (OPMs) require precise parameter tuning for optimal sensitivity.

Purpose of the Study:

  • To apply automated machine learning strategies for optimizing a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM).
  • To enhance the sensitivity of SERF OPMs through efficient parameter control.

Main Methods:

  • Utilized automated machine learning (ML) strategies for OPM parameter optimization.
  • Employed direct noise floor measurements and indirect on-resonance demodulated gradient measurements to guide optimization.
  • Focused on optimizing operational parameters of the SERF OPM.

Main Results:

  • Achieved a significant increase in optimal OPM sensitivity, improving from 500 fT/Hz to below 109 fT/Hz.
  • Demonstrated the viability of both direct and indirect measurement methods for sensitivity optimization.
  • ML approach proved efficient in navigating complex parameter landscapes.

Conclusions:

  • Automated ML strategies are highly effective for optimizing SERF OPM sensitivity.
  • The developed ML approach provides a flexible and efficient tool for benchmarking OPM hardware advancements.
  • This method can accelerate the development of next-generation OPM sensors.