Superflares are large explosive events on stellar surfaces one to six orders-of-magnitude larger than the largest flares observed on the Sun throughout the space age. Due to the huge amount of energy released in these superflares, it has been speculated if the underlying mechanism is the same as for solar flares, which are caused by magnetic reconnection in the solar corona. Here, we analyse observations made with the LAMOST telescope of 5,648 solar-like stars, including 48 superflare stars. These observations show that superflare stars are generally characterized by larger chromospheric emissions than other stars, including the Sun. However, superflare stars with activity levels lower than, or comparable to, the Sun do exist, suggesting that solar flares and superflares most likely share the same origin. The very large ensemble of solar-like stars included in this study enables detailed and robust estimates of the relation between chromospheric activity and the occurrence of superflares.
Aims. This study aims to improve our understanding of the occurrence and origin of grand solar maxima and minima. Methods. We first investigate the statistics of peaks and dips simultaneously occurring in the solar modulation potentials reconstructed using the Greenland Ice Core Project (GRIP) 10 Be and IntCal13 14 C records for the overlapping time period spanning between ∼1650 AD to 6600 BC. Based on the distribution of these events, we propose a method to identify grand minima and maxima periods. By using waiting time distribution analysis, we investigate the nature of grand minima and maxima periods identified based on the criteria as well as the variance and significance of the Hale cycle during these kinds of events throughout the Holocene epoch. Results. Analysis of grand minima and maxima events occurring simultaneously in the solar modulation potentials, reconstructed based on the 14 C and the 10 Be records, shows that the majority of events characterized by periods of moderate activity levels tend to last less than 50 years: grand maxima periods do not last longer than 100 years, while grand minima can persist slightly longer. The power and the variance of the 22-year Hale cycle increases during grand maxima and decreases during grand minima, compared to periods characterized by moderate activity levels. Conclusions. We present the first reconstruction of the occurrence of grand solar maxima and minima during the Holocene based on simultaneous changes in records of past solar variability derived from tree-ring 14 C and ice-core 10 Be, respectively. This robust determination of the occurrence of grand solar minima and maxima periods will enable systematic investigations of the influence of grand solar minima and maxima episodes on Earth's climate.
Among the eruptive activity phenomena observed on the Sun, the most technology threatening ones are flares with associated coronal mass ejections (CMEs) and solar energetic particles (SEPs). Flares with associated CMEs and SEPs are produced by magnetohydrodynamical processes in magnetically active regions (ARs) on the Sun. However, these ARs do not only produce flares with associated CMEs and SEPs, they also lead to flares and CMEs, which are not associated with any other event. In an attempt to distinguish flares with associated CMEs and SEPs from flares and CMEs, which are unassociated with any other event, we investigate the performances of two machine learning algorithms. To achieve this objective, we employ support vector machines (SVMs) and multilayer perceptrons (MLPs) using data from the Space Weather Database of Notification, Knowledge, Information (DONKI) of NASA Space Weather Center, the Geostationary Operational Environmental Satellite (GOES), and the Space-Weather Heliospheric and Magnetic Imager Active Region Patches (SHARPs). We show that True Skill Statistics (TSS) and Heidke Skill Scores (HSS) calculated for SVMs are slightly better than those from the MLPs. We also show that the forecasting time frame of 96 hours provides the best results in predicting if a flare will be associated with CMEs and SEPs (TSS=0.92±0.09 and HSS=0.92±0.08). Additionally, we obtain the maximum TSS and HSS values of 0.91±0.06 for predicting that a flare will not be associated with CMEs and SEPs for the 36 hour forecast window, while the 108 hour forecast window give the maximum TSS and HSS values for predicting CMEs will not be accompanying any events (TSS=HSS=0.98±0.02).
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