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26 April 2024

» arxiv » 1606.1587

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A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast
Yuko Hada-Muranushi ; Takayuki Muranushi ; Ayumi Asai ; Daisuke Okanohara ; Rudy Raymond ; Gentaro Watanabe ; Shigeru Nemoto ; Kazunari Shibata ;
Date:	6 Jun 2016
Abstract:	Automated forecasts serve important role in space weather science, by providing statistical insights to flare-trigger mechanisms, and by enabling tailor-made forecasts and high-frequency forecasts. Only by realtime forecast we can experimentally measure the performance of flare-forecasting methods while confidently avoiding overlearning. We have been operating unmanned flare forecast service since August, 2015 that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We report the method and prediction results of the system.
Source:	arXiv, 1606.1587
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