A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast [SSA]

http://arxiv.org/abs/1606.01587


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.

Read this paper on arXiv…

Y. Hada-Muranushi, T. Muranushi, A. Asai, et. al.
Tue, 7 Jun 16
13/80

Comments: 6 pages, 4 figures

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