This paper reports an all-optical vector magnetometer enhanced by a machine learning model. Using a dual probing beam setup, spin projections in both probe directions are simultaneously detected. Vector information is directly obtained from the measured phases of spin projection signals. To enhance the measurement accuracy and mitigate the dead zone effect, we introduce an artificial neural network (ANN) to link the phase signals to the field direction. With the addition of amplitude information to the ANN input, the average angle error is reduced to less than 0.3° within a hemisphere. Furthermore, this configuration demonstrates a field angle sensitivity of better than 30 μ rad/Hz1/2.

中文翻译：

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由人工神经网络实现的增强型全光矢量原子磁力计


本文报道了一种由机器学习模型增强的全光矢量磁力计。使用双探测光束设置，可以同时检测两个探头方向上的自旋投影。矢量信息直接从自旋投影信号的测量相位中获得。为了提高测量精度并减轻盲区效应，我们引入了人工神经网络 （ANN） 将相位信号与磁场方向联系起来。通过将振幅信息添加到 ANN 输入中，半球内的平均角度误差减小到 0.3° 以下。此外，这种配置表现出优于 30 μ rad/Hz1/2 的场角灵敏度。