Alkali-metal-noble-gas comagnetometers operating in the spin exchange relaxation free (SERF) regime have been widely used in frontier science research and navigation technology. Under normal circumstances, the comagnetometer operates at the compensation point so that any interfering external magnetic field is tracked and suppressed by the nuclear spin. This in turn can greatly diminish the comagnetometer's sensitivity to the magnetic field; however, its sensitivity to rotations and to anomalous fields is still maintained. The compensation point is typically adjusted manually, which leads to low accuracy and compensation speed, in addition to only allowing short-term magnetic field stability. In order to account for these problems, we designed a three-axis magnetic field compensation system in real time based on LabVIEW, and employed it to a K-Rb-21Ne comagnetometer. Our study shows that by using a fuzzy-proportional-integral (Fuzzy-PI) controller, the compensation point can be tracked in real time and with higher precision when compared with manually adjusted methods, therefore improving the magnetic field stability and sensitivity of the comagnetometer. In addition, the real-time magnetic compensation system can also be widely used in magnetometers.
Diffraction beams produced by an acousto-optic modulator are widely used in various optical experiments, some of which need to modulate the radio-frequency drive signal to change the diffraction beams from continuous light to pulsed light. The generation of such pulsed light is open-loop, and long-term stability of the power is disregarded. In this paper, we introduce a method to suppress the pulsed light power drift of a semiconductor laser. By using the servo system, the low frequency power drift of 1–60 kHz pulsed light can be suppressed. This pulsed light power stabilization method can be applied to optical rotation detection and pulse pumping.
The fringe noises disrupt the precise measurement of the atom distribution in the process of the absorption images. The fringe removal algorithms have been proposed to reconstruct the ideal reference images of the absorption images to remove the fringe noises. However, the focus of these fringe removal algorithms is the association of the fringe removal performance with the physical systems, leaving the gap to analyze the workflows of different fringe removal algorithms. This survey reviews the fringe removal algorithms and classifies them into two categories: the image-decomposition based methods and the deep-learning based methods. Then this survey draws the workflow details of two classical fringe removal algorithms, and conducts experiments on the absDL ultracold image dataset. Experiments show that the singular value decomposition (SVD) method achieves outstanding performance, and the U-net method succeeds in implying the image inpainting idea. The main contribution of this survey is the interpretation of the fringe removal algorithms, which may help readers have a better understanding of the research status. Codes in this survey are available at https://github.com/leigaoyi/Atomic_Fringe_Denoise.
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