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Light bridges (LBs) are narrow structures dividing sunspot umbra, and their role in active region evolution is yet to be explored. We investigated the magnetic structure of the two LBs: a narrow LB (with width ∼810 km) and a considerably wider LB (2475 km) in the active region NOAA 12371. We employed: (1) the high-spatial-resolution spectropolarimetric data obtained by the Near InfraRed Imaging Spectropolarimeter (NIRIS) of the 1.6 m Goode Solar Telescope (GST) for studying the magnetic structure at the photosphere, and (2) the nonlinear force-free field (NLFFF) models, extrapolated from both the photospheric magnetogram from GST/NIRIS and from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, for studying the three-dimensional (3D) magnetic structure on a larger scale. Our observations reveal the presence of a field-free (or, more precisely, weak-field) region and the different velocity structures inside the two LBs. Analysis of the 3D NLFFF model shows a low-lying magnetic canopy as well as the enhanced current system above the LBs. The substantial difference between the LBs and the umbrae is found in the overall magnetic topology in that the field lines emanating from the two LBs are more twisted than that from the neighboring umbrae.

 

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.

 

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.