ﻻ يوجد ملخص باللغة العربية
Precession frequencies measured by optically pumped scalar magnetometers are dependent on the relative angle between the sensor and the external magnetic field. This dependence is known to be induced mainly by the nonlinear Zeeman effect and the orientation-dependent light shift, resulting in the so-called heading errors if the magnetic field orientation is not well known or is not stable. In this work, we find that the linear nuclear Zeeman effect has also a significant impact on the heading errors. It not only shifts the precession frequency but causes asymmetry: the heading error for sensors orienting in the upper-half plane with respect to the external field is different from the case when the sensors work in the lower-half plane. This heading error also depends on the relative direction of the probe laser to the driving magnetic field. With a left-handed circularly-polarized pump laser, when the probe laser is parallel to the driving field, the angular dependence of the precession frequency is smaller when the sensor is in the upper plane. Otherwise, when they are perpendicular to each other, the heading error is smaller when the sensor is in the lower plane. Furthermore, to suppress the heading error, we propose to utilize a small magnetic field along the propagation direction of the pump laser. By tuning the magnitude of this auxiliary field, the heading-error curve is flattened around different angles, which can increase the accuracy in practice when the magnetometer works around a certain orientation angle.
The nonlinear Zeeman effect can induce splitting and asymmetries of magnetic-resonance lines in the geophysical magnetic field range. This is a major source of heading error for scalar atomic magnetometers. We demonstrate a method to suppress the non
When optically pumped magnetometers are aimed for the use in Earths magnetic field, the orientation of the sensor to the field direction is of special importance to achieve accurate measurement result. Measurement errors and inaccuracies related to t
Mid-circuit measurement and reset are crucial primitives in quantum computation, but such operations require strong interactions with selected qubits while maintaining isolation of neighboring qubits, which is a significant challenge in many systems.
We demonstrate identification of position, material, orientation and shape of objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information ex
Our 2005 Physical Review Letter entitled Suppression of Spin-Projection Noise in Broadband Atomic Magnetometry (volume 94, 203002) relied heavily in its claims of experimental quantum-limited performance on the results of a prior publication from our