We have developed the automatic detection scheme for upper hybrid resonance (UHR) frequency using a convolutional neural network (CNN) from the electric field spectra obtained by the plasma wave experiment (PWE) aboard Arase. In this paper, we investigate the practical capability of this scheme in terms of actual scientific use case. We find that the average error rate is below 7.8% when the wave frequency is above 30 kHz and the wave spectral intensity is less than 10⁻⁵ mV 2/m²/Hz. About 91% of the data obtained by the high‐frequency analyzer (HFA) aboard the Arase satellite satisfies these conditions. To improve the accuracy of the determined UHR frequencies in a wide frequency range, we used another receiver, the onboard frequency analyzer (OFA), which enables us to detect low‐frequency UHR emissions. We confirmed that the averaged error rate derived by the OFA spectra becomes better than that derived from the HFA spectra in a frequency range below 20 kHz. We report the performance of the UHR frequency determination under the different geomagnetic conditions. We find that the UHR frequency can be determined with good accuracy using the CNN from the frequency‐time diagram both during geomagnetically quiet and disturbed conditions. We conclude that the CNN‐based UHR frequency determination is a reliable method to derive the electron density along the satellite orbit through observations of UHR frequencies, and this method contributes to studies on dynamics of the plasmasphere.

中文翻译：

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卷积神经网络从Arase / PWE数据中检测UHR频率

我们已经从卷积神经网络（CNN）通过Arase上的等离子波实验（PWE）获得的电场谱中开发了一种上混合共振（UHR）频率的自动检测方案。在本文中，我们根据实际的科学用例研究了该方案的实用能力。我们发现，当波频率高于30 kHz且波谱强度小于10 ^-5  mV  2 / m ²时，平均错误率低于7.8％。/赫兹。通过Arase卫星上的高频分析仪（HFA）获得的数据中约有91％满足这些条件。为了提高在宽频率范围内确定的UHR频率的准确性，我们使用了另一个接收器，即车载频率分析仪（OFA），它使我们能够检测低频UHR发射。我们确认，在低于20 kHz的频率范围内，通过OFA频谱得出的平均错误率变得比从HFA频谱得出的平均错误率更好。我们报告了在不同地磁条件下UHR频率确定的性能。我们发现，在地磁安静和受干扰的情况下，都可以使用频率-时间图上的CNN来精确确定UHR频率。