Flare Transformer: Solar Flare Prediction using Magnetograms and Sunspot Physical Features
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- KANEDA Kanta
- Keio University
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- IIDA Tsumugi
- Keio University
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- NISHIZUKA Naoto
- National Institute of Information and Communications Technology
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- KUBO Yuki
- National Institute of Information and Communications Technology
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- SUGIURA Komei
- Keio University
Bibliographic Information
- Other Title
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- Flare Transformer: 磁場画像と物理特徴量を用いた太陽フレア予測
Abstract
<p>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.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2022 (0), 2D6GS201-2D6GS201, 2022
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390292706092148864
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed