Detection of UHR Frequencies by a Convolutional Neural Network From Arase/PWE Data

Abstract

AbstractWe 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.