The κ distribution function is fitted to the entire data set of MErcury Surface, Space ENvironment, GEochemistry and Ranging's (MESSENGER) 1‐min Fast Imaging Plasma Spectrometer (FIPS Andrews et al., 2007, https://doi.org/10.1007/s11214‐007‐9272‐5) proton spectra, and then artificial neural networks (ANNs) are used to assess the quality of this fit to the data. The κ distribution function is fitted to each proton spectrum using the downhill‐simplex method, providing an estimate for density, n , temperature, T , and the κ parameter, which controls the shape of the distribution. The final trained neural network achieved classification accuracy of 96% and has been used to classify the 1‐min proton data set collected during MESSENGER's ∼4 years in orbit of Mercury. Of the 223,282 spectra, ∼160,000 were classified as having “good” fitting κ distributions, ∼133,000 of which were measurements obtained from within the magnetosphere, and ∼18,000 were from the magnetosheath.

中文翻译：

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一种对MESSENGER FIPS质子光谱进行分类的机器学习方法

该κ分布函数被装配到水银面的整个数据集，空间环境，地球化学和测距的（MESSENGER）1分钟快速成像等离子体分光计（FIPS Andrews等人，2007，https://doi.org/10.1007/ s11214‐007‐9272‐5）质子谱，然后使用人工神经网络（ANN）评估与数据拟合的质量。使用下坡简化方法将κ分布函数拟合到每个质子谱，从而提供密度，n，温度，T和κ的估计值参数，它控制分布的形状。经过最终训练的神经网络实现了96％的分类精度，并已用于对MESSENGER在水星轨道上约4年内收集的1分钟质子数据集进行分类。在223,282个光谱中，约有160,000个被分类为具有“良好”拟合κ分布，其中约133,000个是从磁层内部获得的测量值，约18,000个是从磁层获得的。