Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. 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. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) 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; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) 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. 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.
We study the role of the coronal magnetic field configuration of an active region in determining the propagation direction of a coronal mass ejection (CME). The CME occurred in the active region 11944 (S09W01) near the disk center on 2014 January 7 and was associated with an X1.2 flare. A new CME reconstruction procedure based on a polarimetric technique is adopted, which shows that the CME changed its propagation direction by around 28 • in latitude within 2.5 R ⊙ and 43 • in longitude within 6.5 R ⊙ with respect to the CME source region. This significant non-radial motion is consistent with the finding of Möstl et al. (2015). We use nonlinear force-free field (NLFFF) and potential field source surface (PFSS) extrapolation methods to determine the configurations of the coronal magnetic field. We also calculate the magnetic energy density distributions at different heights based on the extrapolations. Our results show that the active region coronal magnetic field has a strong influence on the CME propagation direction. This is consistent with the "channelling" by the active region coronal magnetic field itself, rather than deflection by nearby structures. These results indicate that the active region coronal magnetic field configuration has to be taken into account in order to determine CME propagation direction correctly.
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