日本地球惑星科学連合2019年大会

講演情報

[E] ポスター発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM13] 内部磁気圏

2019年5月29日(水) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:海老原 祐輔(京都大学生存圏研究所)、Danny Summers(Memorial University of Newfoundland)、三好 由純(名古屋大学宇宙地球環境研究所)、齊藤 慎司(名古屋大学 大学院理学研究科)

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

*松田 昇也1長谷川 達人2熊本 篤志3土屋 史紀3笠原 禎也4三好 由純5笠羽 康正3松岡 彩子1 (1.宇宙航空研究開発機構 宇宙科学研究所、2.福井大学、3.東北大学、4.金沢大学、5.名古屋大学 宇宙地球環境研究所)

キーワード:あらせ衛星、UHR周波数、機械学習

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.