Abstract
In this study, we developed a technique for automatically determining upper hybrid resonance (UHR) frequencies using a convolutional neural network (CNN) to derive the electron density along the orbit of the Arase satellite. We used three CNN models (AlexNet, VGG16 and ResNet) to determine the UHR frequencies without additional features based on an expert's knowledge. We also reproduced the multi-layer perceptron (MLP) model that had been used for the Van Allen probes mission, which requires observed electric field spectra and additional five features (i.e., decimal logarithm of electron cyclotron frequency (log 10 f ce ), L-value, geomagnetic index (K p ), magnetic local time, and frequency bin with the highest power spectral density from the electric field spectra (fbin max )). We confirmed that the proposed method using CNN more accurately determined the UHR frequencies than did the conventional method. The mean absolute error (MAE) of the VGG16 model was 3.478 bins when the input vector comprised both the observed electric field spectrum and the additional five features. In contrast, the MAE of the conventional method was 5.986 bins (72.1% worse). Moreover, we confirmed that the proposed method achieves a high accuracy regardless of the use of the additional five features (the MAE of the ResNet model was 3.664 bins when excluding the additional five features). This suggests that the feature map of the ResNet model acquired a representation ability beyond the five features.
Highlights
It is well known that ambient electron density is an important property of space plasma
We describe our machine learning approach and discuss results for the upper hybrid resonance (UHR) frequency determination from electric field spectra observed by the plasma wave experiment (PWE)/high frequency analyzer (HFA)
We reproduced the method proposed in a related study [14] as a baseline for comparing the estimation accuracy with that of our proposed method. zMLP uses the following five features, which are based on expert knowledge: (1) decimal logarithm of electron cyclotron frequency, (2) L-value, (3) geomagnetic activity index (Kp), (4) magnetic local time, and (5) frequency bin with the highest power spectral density from the electric field spectra (f binmax)
Summary
It is well known that ambient electron density is an important property of space plasma. The radial structures of the terrestrial ionosphere and plasmasphere are characterized by variations of ambient electron density. The outer edge of the plasmasphere is called the plasmapause and/or plasmasphere boundary layer and is defined as the location of a sudden decrease in ambient electron density [1], [2]. The location of the plasmapause varies according to geomagnetic conditions and is well correlated with geomagnetic indices (Kp), as demonstrated by Carpenter and Anderson [3]. The associate editor coordinating the review of this manuscript and approving it for publication was Wu-Shiung Feng. The evolution of the plasmasphere is important when studying the role of a geomagnetic storm in geospace
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