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

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/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/Hz to <109fT/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.

Portable single-beam cesium zero-field magnetometer for magnetocardiography

“…The response of light transmission to the magnetic field is directionally dependent on the static magnetic field, as described in Ref. 15. As part of the calibration routine, the zero-field magnetometer utilizes its directional sensitivity to detect and cancel static residual magnetic fields near the sensor in three axes, applying values of B x0 , B y0 , and B z0 , the magnetic field values required to cancel static residual fields in the environment.…”

“…All operational parameters for the prototype sensor are optimized for sensitivity performance using the methods described in Ref. 15. The sensor has a flat frequency response to applied fields out to at least 250 Hz.…”

“…[11][12][13][14] The OPM described herein utilizes the Hanle effect to detect magnetic signals. 15 This principle describes the effect of an atomic vapor on the absorption of laser light when the atoms experience a magnetic field. The atomic vapor is pumped and probed using a single laser beam, and the change of the light absorption is detected through an on-board photodiode (PD).…”

Sub-Doppler dichroism as a useful tool in alkali atom hyperfine spectroscopy

“…Hyperfine transitions of alkali atoms are widely used in atomic physics applications [1][2][3][4][5]. In this regard, spectroscopy of the D1 and D2 lines of these atoms, which represent S 1/2 -P 1/2 and S 1/2 -P 3/2 transitions, are of great interest to atomic physicists.…”