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Solar Cycle 24 is a particularly weak cycle and there are only six super active regions (SARs). Except for NOAA active region (AR) 12192, all other SARs are coronal mass ejection rich (CME-rich) and produced CMEs faster than 1000 km s − 1 . By studying the similarities and differences between CME-poor SARs and CME-rich SARs in Solar Cycle 24, we aim to improve our understanding of the cause of major explosive solar activity events. Using the magnetograms of the Helioseismic and Magnetic Imager (HMI) and the images of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), the magnetic-field characteristics of the six SARs and their established overall or even global magnetic connectivity are studied. It is found that the area of strong magnetic shear ( A Ψ ) of all the six SARs is larger than 100 Mm 2 , and the composite magnetic-field index ( I com : Chen and Wang in Astron. Astrophys.543, A49, 2012) of them is higher than 1. However, the total free magnetic-energy density ( E free ) and the I com for the majority of them are relatively low compared to the SARs in Solar Cycles 22 and 23. Compared with their very high total unsigned magnetic flux, the CME-poor AR 12192 has a lower E free , a shorter magnetic-neutral line with steep horizontal gradient, a smaller A Ψ , and a lower I com . It is also revealed that the current helicity proxy, in terms of either the twist parameter or the best force-free parameter, has a good correlation with the fastest CME velocities of the SARs and serves as a good criterion to distinguish the CME-poor SARs from the CME-rich SARs. Except for NOAA AR 12192, all other SARs are found to connect closely with other ARs, and the AR cluster is a common feature of CME-rich SARs. [ABSTRACT FROM AUTHOR]

Solar flares are one of the most important sources of disastrous space weather events, leading to negative effects on spacecrafts and living organisms. It is very important to predict solar flares to minimize the potential losses. In this paper, we use three different machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost (XGB) to predict the total flare index T flare and the maximum flare index M flare of an active region (AR) within the subsequent of 24, 48, and 72 hrs. First, we selected 54514 vector magnetograms of 129 ARs on the visible solar hemisphere in solar cycle 24 whose maximum sunspot groups' area was larger than 400 μh. Then the following four magnetic parameters of each magnetogram were calculated: 1) the total magnetic flux | Φ tot | , 2) the total photospheric free magnetic energy density E free , 3) the gradient-weighted integral length of the neutral line with horizontal magnetic gradient of line-of-sight magnetic field larger than 0.1 G km − 1 ( WL SG ), and 4) the area with magnetic shear angle larger than 40 ∘ ( A Ψ ), as well as T flare and M flare corresponding to each magnetogram. Afterward, we split samples randomly into training (85% of the whole data) and testing (15%) data sets. After hyperparameter tuning and model construction we found that RF is an optimal algorithm for the prediction task and that the coefficients of determination ( R 2 ) of test data set via the majority of RF models are beyond 0.97. In addition, the feature importance of RF and XGB models indicates that | Φ tot | and E free are two optimal parameters to predict both T flare and M flare , and | Φ tot | and E free are the best parameters for M flare and T flare , respectively. [ABSTRACT FROM AUTHOR]