Monitoring the plasmasphere is an important task to achieve in the Space Weather context. A consolidated technique consists of remotely inferring the equatorial plasma mass density in the inner magnetosphere using Field Line Resonance (FLR) frequencies estimates. FLR frequencies can be obtained via cross‐phase analysis of magnetic signals recorded from pairs of latitude separated stations. In the last years, machine learning (ML) has been successfully applied in Space Weather, but this is the first attempt to estimate FLR frequencies with these techniques. We survey several supervised ML algorithms for identifying FLR frequencies by using measurements of the European quasi‐Meridional Magnetometer Array. Our algorithms take as input the 2‐hour cross‐phase spectra of magnetic signals and return the FLR frequency as output; we evaluate the algorithm performance on four different station pairs from L = 2.4 to L = 5.5. Results show that tree‐based algorithms are robust and accurate models to achieve this goal. Their performance slightly decreases with increasing latitude and tend to deteriorate during nighttime. The estimation error does not seem to depend on the geomagnetic activity, although at high latitudes the error increases during highly disturbed geomagnetic conditions such as the main phase of a storm. Our approach may represent a prominent space weather tool included into an automatic monitoring system of the plasmasphere. This work represents only a preliminary step in this direction; the application of this technique on a more extensive data set and on more pairs of stations is straightforward and necessary to create more robust and accurate models.

中文翻译：

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评估机器学习技术以从相谱中识别出场线共振频率

监测等离子层是在空间天气背景下要完成的重要任务。一项综合技术包括使用场线共振（FLR）频率估算值远程推断内部磁层中的赤道等离子体质量密度。FLR频率可以通过对从纬度分离的站点对记录的磁信号进行交叉相位分析来获得。在过去的几年中，机器学习（ML）已成功地应用于“太空天气”，但这是使用这些技术估算FLR频率的首次尝试。我们使用欧洲准子午线磁力计阵列的测量方法，调查了几种用于监督FLR频率的监督ML算法。我们的算法将磁信号的2小时交叉相位谱作为输入，并返回FLR频率作为输出。L  = 2.4至L  = 5.5。结果表明，基于树的算法是实现此目标的可靠且准确的模型。随着纬度的增加，它们的性能会略有下降，并且在夜间往往会变差。估计误差似乎并不取决于地磁活动，尽管在高纬度地区，在诸如风暴主相之类的高度受干扰的地磁条件下，误差会增加。我们的方法可能代表了等离子体球自动监测系统中所包含的一种杰出的太空气象工具。这项工作只是朝这个方向迈出的第一步。在更广泛的数据集和更多的站点对上应用此技术非常简单，而且对于创建更强大且更准确的模型是必要的。