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This situation points to the need for better identification of a unique physical parameter space in which solar flares occur, for identifying the relevant physical trigger (or triggers), for the ability to estimate the likelihood that a trigger's presence will produce an event, and what size an event is expected. Such shortcomings may be of most serious consequence in the context of extreme solar flares-situations where a catastrophic flare event is highly likely, but when it will occur is both unknown and of great importance. When faced with specific questions regarding timing and magnitude of impending flares in the context of a flare-productive sunspot group, short-range targeted predictions are beyond the present science's capabilities. Today's probabilistic flare forecasting capability is arguably not very good. Their fast initiation and orders-of-magnitude increase in high-energy electromagnetic radiation require true forecasting efforts. Solar flares are one of the primary initiators of many space weather phenomena.
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Leka, Graham Barnes, in Extreme Events in Geospace, 2018 Abstract Case analyses demonstrate that the deep learning-based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions. The performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. The performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 h). The testing results of the forecasting model indicate that the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data furthermore, these forecasting patterns are robust to the noise in the observational data. Therefore, the prediction of solar flares is transformed into a two-category problem. The paper uses the CNN network structure to perform solar flare prediction, which means we process the input magnetic map through the CNN and predict whether flares will occur. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. The triggering mechanism for these flares, however, remains unknown. Solar flares originate from the release of the energy stored in the magnetic field of solar active regions. Linghe Kong, in Big Data in Astronomy, 2020 4.2.3 Deep learning We were constantly changing light sources, its temperature and managed to achieve absolutely unique sun splash pictures of high quality which will make your simple photo look like an incredible piece of art.Yatong Chen.
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