RESUMO
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.
RESUMO
We present an unshielded, double-resonance magnetometer in which we have implemented a feed-forward measurement scheme in order to suppress periodic magnetic noise arising from, and correlated with, the mains electricity alternating current line. The technique described here uses a single sensor to track ambient periodic noise and feed forward to suppress it in a subsequent measurement. This feed forward technique has shown significant noise suppression of electrical mains-noise features of up to 22 dB under the fundamental peak at 50 Hz, 3 dB at the first harmonic (100 Hz), and 21 dB at the second harmonic (150 Hz). This technique is software based, requires no additional hardware, and is easy to implement in an existing magnetometer.
