Unsupervised learning of active-region nesting on the Sun

Abstract

Active-region emergence on the Sun shows a degree of clumpiness in both space and time. At a given time, multiple active regions can be seen in what is called active-region- or sunspot-group nests. This tendency also increases the potential to produce large flares and associated CMEs. In the literature, the nesting tendency of active regions is reported in the range of 30-50 per cent, but no statistically robust and ML-based approaches exist so far. Quantifying the nesting degree along an activity cycle and determining its spatial and temporal scales are important to investigate the processes that cause this phenomenon. In this study, we estimate the latitudinal and longitudinal extents of active region nesting using both continuum and magnetogram data, using SDO/HMI synoptic magnetograms and Kislovodsk Mountain Astronomical Station (KMAS) sunspot group data. We carry out kernel density estimation (Fig. 1) and unsupervised ML techniques (e.g., DBSCAN and Gaussian mixtures) in spatial and spatio-temporal domains. Our study reveals trends in the emergence characteristics of sunspot groups on the Sun.Figure 1: Kernel density estimation with a Gaussian kernel on the time-longitude plane. The dot size indicates sunspot group areas in MSH.