Magnetic Characteristics of Sunspot Groups and their Role in Producing Adverse Space Weather - MCCLOSKEY, AOIFE ELIZABETH (Trinity College Dublin. School of Physics. Discipline of Physics, 2019)
The energy that powers solar ﬂares is known to come from magnetic energy stored in sunspot groups, but the precise conditions required and processes involved in energy release remain unclear. The likelihood of ﬂaring is directly related to the complexity of the magnetic ﬁeld, such that larger, more complex sunspot groups produce larger more frequent ﬂares than smaller, simple sunspot groups. The research in this thesis aims to investigate the evolution of sunspot complexity to gain greater understanding of how sunspot groups produce ﬂares, with the intention of improving upon current methods of ﬂare prediction. Firstly, the temporal evolution of sunspot groups in terms of their white-light structural complexity (McIntosh classiﬁcation) was examined during Solar Cycle 22 (SC22). It was found that over a 24-hr period the majority,??60%, of sunspot groups do not evolve in terms of the parameters described by the McIntosh system. For the ﬁrst time, evolution-dependent 24-hr ﬂaring rates were calculated and higher ﬂaring rates were observed for practically all McIntosh classes for upward evolution with opposite behaviour observed for downward evolution (McCloskey et al, 2016). These results agree qualitatively with previous studies relating sunspot area change (i.e., ﬂux emergence/decay) and penumbral asymmetry (i.e., higher degree of magnetic ﬁeld twisting) to ﬂare production. Following on from this, ﬂaring rates calculated from sunspot-group evolution in the full McIntosh classiﬁcation were used to develop a new forecasting method under the assumption of Poisson statistics(McCloskey et al, 2018). It was found that the new evolution-dependent method outperforms the older, point-in-time Poisson method. However, a tendency for over-forecasting was found for both of the Poisson-based methods when using SC22 training data applied to the testing period of SC23. A comparison of Cycle-to-Cycle ﬂaring rates was carried out, with results indicating that the ﬂaring statistics between cycles is diﬀerent (i.e., SC23 shows decreased activity compared to SC22). Machine-learning techniques were then employed on the evolution of sunspot group McIntosh classiﬁcations for the ﬁrst time, to construct new ﬂare forecasting models. A direct comparison to the evolution-based McIntosh-Poisson method was carried out and it was shown that the majority of these machine-learning models surpassed its performance. However, a similar tendency for over-forecasting was found, strengthening the ﬁndings that the ﬂaring activity between Solar Cycles is decreasing. Finally, the relative importance of the McIntosh classiﬁcation components was then examined and it was found that the most important features were the end-state compactness (i.e., a proxy for total magnetic ﬂux), with the next most important features being either the end-state modiﬁed Zurich class or the starting compactness class. These combinations of pairings indicate that either the end-state total magnetic ﬂux or the emergence of ﬂux within the interior of a sunspot group (independent of group size) provides the largest contribution to predicting whether a sunspot group will be ﬂare-productive or not. The novel research carried out in this thesis has explored the temporal evolution of sunspot group classiﬁcations and their relation to solar ﬂare production. The collection of results presented here has succeeded in quantifying this relationship for the ﬁrst time, showing that evolution in sunspot-group structural characteristics are inherently linked to ﬂare productivity. Future research that attempts to provide further understanding of this relationship will be crucial for the continued improvement of ﬂare forecasting models and their utility for the space weather forecasting community as a whole.
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Keywords: Solar Physics; Astrophysics; Space Weather; Solar Flare; Forecasting; Sunspots