In-Situ Measurement of Electrical-Heating-Induced Magnetic Field for an Atomic Magnetometer

Repumping atomic media for an enhanced sensitivity atomic magnetometer

Atomic vapour magnetometers sense the local magnetic field strength by measuring the resulting precession rate of a well-defined quantum state. An essential prerequisite for this approach is a requirement to drive the media into this quantum state, which is frequently achieved via optical pumping. In real-world alkali-metal atoms, with their multiplicity of ground states, the optical pumping process is necessarily lossy, with a large fraction of the atoms being lost to quantum states that do not contribute to the useful magnetically sensitive signal. This consequently reduces the sensitivity of all optically-pumped atomic sensors. Here we theoretically and experimentally study the population changes of the quantum ground states of ⁸⁷Rb during optical pumping. We use this understanding to develop a repumping scheme that allows us to increase the number of atoms that are contributing to the useful magnetic sensing output. Unlike prior schemes, our approach delivers this improved sensitivity with significantly suppressed fictitious magnetic fields associated with the repumping, which would otherwise reduce the accuracy of the sensor. When operated at Earth's field strength (∼50µT), the repumped sensor shows a magnetic sensitivity of 200 fT/Hz, that is nearly three times higher than the non-repumped version.

The influence of modulated magnetic field on light absorption in SERF atomic magnetometer

A single-beam spin-exchange relaxation-free atomic magnetometer is ultra-sensitive in the zero field, which has great potential for the detection of a magnetoencephalogram. The addition of a modulated magnetic field is an important approach to achieve high sensitivity for devices of this kind. In this study, we discovered that the amplitude and frequency of the modulated magnetic field (modulation index 0-3) both influence the light absorption. We defined this effect into a function by combining theoretical analysis and the results of experiments. It is discovered that the transmission intensity decreases with an increase in the modulation index. This effect is weakened under the application of a high modulation index. In addition, the transmission intensity and bias magnetic field no longer follow a strict Lorentz curve, while a high degree of fit can be achieved by applying the numerical solution of the Bloch function. A compact magnetometer with a volume of 10 cm³ and a sensitivity of 20 fT/Hz is developed based on the single beam scheme for the proof of concept. Our study is crucial in two aspects: (1) Obtaining high sensitivity through a short measurement period and (2) alignment of the scale factor of the individual magnetometer in a detection array, which further pave the way for improvement in a magnetometer's performance under a variety of optics platforms.

A genetic algorithm and backpropagation neural network based temperature compensation method of spin-exchange relaxation-free co-magnetometer

This paper presents a temperature compensation method based on the genetic algorithm (GA) and backpropagation (BP) neural network to reduce the temperature induced error of the spin-exchange relaxation-free (SERF) co-magnetometer. The fluctuation of the cell temperature results in the variation of the optical rotation angle and the probe light absorption. The temperature fluctuation of the magnetic field shielding layer induces the variation of the magnetic field. In addition, one of the causes of light power variation is temperature fluctuation of the optical element. In summary, temperature fluctuations cause a variety of SERF co-magnetometer errors, and the relationship between these errors and temperature fluctuations has the characteristics of time-variance and non-linearity. There are two kinds of methods to suppress these errors. One way is to reduce temperature fluctuations of the SERF co-magnetometer. However, this method requires additional hardware and high cost, which are not suitable for miniaturization and low cost applications. Another effective method to suppress nonlinear and time-varying errors is to utilize intelligent algorithms for temperature compensation. In this paper, the BP neural network is applied for temperature compensation, and the GA is utilized to overcome the disadvantages of the BP neural network. The training data were obtained by changing the ambient temperature of the SERF co-magnetometer. The experimental results show that the method proposed in this work can significantly improve the accuracy of the co-magnetometer at complex ambient temperatures, and the stability of the SERF co-magnetometer at room temperature can be improved by at least 45%.