Japan Geoscience Union Meeting 2019

Presentation information

[E] Poster

P (Space and Planetary Sciences ) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM13] Inner magnetosphere: Recent understanding and new insights

Wed. May 29, 2019 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Yusuke Ebihara(Research Institute for Sustainable Humanosphere, Kyoto University), Danny Summers(Memorial University of Newfoundland), Yoshizumi Miyoshi(Institute for Space-Earth Environmental Research, Nagoya University), Shinji Saito(Graduate School of Science, Nagoya University)

[PEM13-P06] Automatic determination of Upper Hybrid Resonance Frequencies by Convolutional Neural Network.

*Shoya Matsuda1, Tatsuhito Hasegawa2, Atsushi Kumamoto3, Fuminori Tsuchiya3, Yoshiya Kasahara4, Yoshizumi Miyoshi5, Yasumasa Kasaba3, Ayako Matsuoka1 (1.Institute of Space and Astronautical Science/Japan Aerospace Exploration Agency, 2.University of Fukui, 3.Tohoku University, 4.Kanazawa University, 5.ISEE, Nagoya University)

Keywords:Arase satellite, UHR frequency, Machine learning

Electron number density is a key parameter for discussions of plasma wave generation/propagation, and wave-particle interaction in the inner magnetosphere. The High Frequency Analyzer (HFA) is a subsystem of Plasma Wave Experiment (PWE) aboard Arase [Kasahara et al. (2018), Kumamoto et al. (2018), Miyoshi et al. (2018)]. The HFA measures electric field spectra in a frequency range from 10 kHz to 10 MHz, which covers a typical frequency range of Upper Hybrid Resonance (UHR) frequency in the inner magnetosphere. Kumamoto et al. (2018) proposed the semiautomatic method for the identification of UHR frequency by computer and a human operator. However, it takes a enormous effort of a human operator.
We propose an automatic determination system of UHR frequency by machine learning. Machine learning is a technique in the field of artificial intelligence to give computers the ability to learn with data. In this study, we defined a task of UHR frequency determination as supervised regression that a computer estimates UHR frequencies using the dataset composed of electric field dynamic spectra with correct UHR frequency labels. We adopted Convolutional Neural Network (CNN) as a machine learning algorithm. In this study, we introduce our machine learning approach and initial results for determining UHR frequency from electric field spectra observed by PWE/HFA.