Takumi ABE

Electron temperature (Te) is an important parameter governing space weather in the upper atmosphere, but has historically been underexplored in the space weather machine learning literature. We present CLARE, a machine learning model for predicting electron temperature in the Earth's plasmasphere trained on AKEBONO (EXOS-D) satellite measurements as well as solar and geomagnetic indices. CLARE uses a classification-based regression architecture that transforms the continuous Te output space into 150 discrete classification intervals. Training the model on a classification task improves prediction accuracy by 6.46% relative compared to a traditional regression model while also outputting uncertainty estimation information on its predictions. On a held out test set from the AKEBONO data, the model's Te predictions achieve 69.67% accuracy within 10% of the ground truth and 46.17% on a known geomagnetic storm period from January 30th to February 7th, 1991. We show that machine learning can be used to produce high-accuracy Te models on publicly available data.

平成29年度宇宙科学に関する室内実験シンポジウム (2018年2月26日-27日. 宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS)相模原キャンパス), 相模原市, 神奈川県 2018 Symposium on Laboratory Experiment for Space Science (February 26-27, 2018. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)), Sagamihara, Kanagawa Japan 資料番号: SA6000123013

平成28年度宇宙科学に関する室内実験シンポジウム (2017年2月27日-28日. 宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS)相模原キャンパス), 相模原市, 神奈川県 2017 Symposium on Laboratory Experiment for Space Science (February 27-28, 2017. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)), Sagamihara, Kanagawa Japan 資料番号: SA6000095012