Double resonance optically pumped magnetometry can be used to measure static magnetic fields with high sensitivity by detecting a resonant atomic spin response to a small oscillating field perturbation. Determination of the resonant frequency yields a scalar measurement of static field (B0) magnitude. We present calculations and experimental data showing that the on-resonance polarimeter signal of light transmitted through an atomic vapour in arbitrarily oriented B0 may be modelled by considering the evolution of alignment terms in atomic polarisation. We observe that the amplitude and phase of the magnetometer signal are highly dependent upon B0 orientation, and present precise measurements of the distribution of these parameters over the full 4π solid angle.

Double-resonance optically pumped magnetometers are an attractive instrument for unshielded magnetic field measurements due to their wide dynamic range and high sensitivity. Use of linearly polarised pump light creates alignment in the atomic sample, which evolves in the local static magnetic field, and is driven by a resonant applied field perturbation, modulating the polarisation of transmitted light. We show for the first time that the amplitude and phase of observed first-and second-harmonic components in the transmitted polarisation signal contain sufficient information to measure static magnetic field magnitude and orientation. We describe a laboratory system for experimental measurements of these effects and verify a theoretical derivation of the observed signal. We demonstrate vector field tracking under varying static field orientations and show that the static field magnitude and orientation may be observed simultaneously, with experimentally realised resolution of 1.7 pT and 0.63 mrad in the most sensitive field orientation.

This paper examines a core leadership strategy for transforming learning and teaching in distance education through flexible and blended learning. It focuses on a project centred on distributive leadership that involves collaboration, shared purpose, responsibility and recognition of leadership irrespective of role or position within an organisation. Distributive leadership was a core principle in facilitating the transformation of learning and teaching through a Teaching Fellowship Scheme that empowered leaders across a regional distance education university. In parallel, a design-based research project analysed the perceptions of the Teaching Fellows in relation to blended learning, time/space, peer learning, innovation and equity issues in relation to distance education

We present an unshielded, double-resonance magnetometer in which we have implemented a feedforward measurement scheme in order to suppress periodic magnetic noise arising from, and correlated with, the mains electricity alternating current (AC) 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.

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.