[P-EM16] 太陽圏・惑星間空間

[PEM16-05] GMDNで観測された宇宙線変動のデータマイニング手法を活用した研究

*小財 正義¹、林 優希²、加藤 千尋²、宗像 一起²、門倉 昭^1,3、片岡 龍峰³、三宅 晶子⁴ (1.情報・システム研究機構　データサイエンス共同利用基盤施設　極域環境データサイエンスセンター、2.信州大学、3.情報・システム研究機構　国立極地研究所、4.茨城工業高等専門学校)

キーワード：銀河宇宙線、太陽変調、宇宙天気

Data-mining approaches are attempted to extract space weather insights from cosmic-ray observation. Ground-based muon observation is sensitive to anisotropies of cosmic rays at approximately 50 GeV and has been operated with great stability for more than 10 years. Space environment influences cosmic-ray anisotropy in various time scales. It will provide scientific topics for which data-mining approaches are leveraged. This data-driven study is expected to lead to a discovery of a new anisotropy profile and provide complementary results with traditional approaches based on visual inspections and physical interpretations. The Global Muon Detector Network (GMDN) started its operation with two-hemisphere detectors at Nagoya (Japan) and Hobart (Australia) in 1992. SaoMartinho da Serra (Brazil) and Kuwait detectors were installed in 2006. GMDN was completed in 2016 by expanding Kuwait detector to a comparable detection area (25 m^2) with Nagoya detector (36 m^2). Each detector records muon counting-rates in multiple directional channels. Temporal resolution is 1-10 minutes in each detector, and 1-hour data is typically used to study the space weather phenomena. Cosmic-ray anisotropies in space are derived by solving the geomagnetic effects and atmospheric propagation by numerical calculations. We are attempting statistical investigation of the anisotropy data accumulated by GMDN, aiming to demonstrate the advantages of the data-mining approaches including unsupervised machine learning or cluster analysis. In this presentation, we will report on the preliminary results.