CLARE: Classification-based Regression for Electron Temperature Prediction
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 u...
| Authors | Michael Liang, Blake DeHaas, Naomi Maruyama et al. |
|---|---|
| Primary category | physics.space-ph |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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