[2D6-GS-2] Machine learning: applications (2)

[2D6-GS-2-01] Flare Transformer: Solar Flare Prediction using Magnetograms and Sunspot Physical Features

〇Kanta Kaneda¹, Tsumugi Iida¹, Naoto Nishizuka², Yuki Kubo², Komei Sugiura¹ (1. Keio University, 2. National Institute of Information and Communications Technology)

Keywords:Solar Flare Prediction, Time-series Forecasting, Transformer

The prediction of solar flares is essential for reducing the potential damage to social infrastructures that are vital to society. However, predicting solar flares accurately is a very challenging task. In this paper, we propose a solar flare prediction model, Flare Transformer, which handles both images and physical features through the Magnetogram Module and the Sunspot Feature Module. We introduce the transformer attention mechanism to model the temporal relationships. We also introduce a new differentiable loss function to balance the two major metrics of the Gandin-Murphy-Gerrity score and Brier skill score. Comparative experiments using Gandin-Murphy-Gerrity score and true skill statistics as metrics showed that the proposed method achieves better performance than baseline methods and human experts.