Department of Physics, Scottish Universities Physics Alliance SUPA, University of Strathclyde, Glasgow G4 0NG, UK.
Sensors (Basel). 2023 Apr 15;23(8):4007. doi: 10.3390/s23084007.
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.
机器学习（ML）是一种有效的工具，可以通过比手动方法更有效地探究复杂系统，以找到最佳参数。对于具有多个参数之间复杂动态和随后大量参数配置的系统，这种效率尤为重要，因为详尽的优化搜索是不切实际的。在这里，我们介绍了一些用于优化单束铯（Cs）自旋交换弛豫自由（SERF）光泵磁力计（OPM）的自动化机器学习策略。通过直接测量噪声底，以及通过测量零场共振的共振解调梯度（mV/nT），间接优化 OPM 的灵敏度（T/Hz）。这两种方法都通过有效控制 OPM 的操作参数，为灵敏度的优化提供了可行的策略。最终，这种机器学习方法将最佳灵敏度从 500 fT/Hz 提高到了 <109 fT/Hz。ML 方法的灵活性和效率可以用于基准测试 SERF OPM 传感器硬件改进，例如单元几何形状、碱金属种类和传感器拓扑结构。
