Image patch analysis of sunspots and active regions

Abstract

Context. Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region's evolution for example. Aims. We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum images. Methods. We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local image patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. Results. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the R-value. Conclusions. Matrix factorization of image patches is a promising new way of characterizing active regions. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.

Differences in the solar cycle variability of simple and complex active regions during 1996–2018

“…Solar active regions (ARs) appear on the Sun's photosphere in a variety of sizes, shapes, and magnetic complexities or magnetic topologies (Howard 1989). A considerable number of studies have used the magnetic complexity of ARs as a measure of their activity (Cortie 1901;Waldmeier 1938;McIntosh 1990;Moon et al 2016). In 1919, Hale et al (1919 introduced the Mount Wilson (or Hale) Classification, which groups ARs according to the distribution of magnetic field topology.…”

On-line Tools for Solar Data Compiled at the Debrecen Observatory and Their Extensions with the Greenwich Sunspot Data

“…Now that the GPR and DPD data are unified, it is edifying to summarize briefly their common history by using historical resources (Scott, 1885;Williams, 1987;Rothermel, 1993;McCrea, 1975;Dezső, 1987), the volumes of GPR, a few recent documents (e. g. volumes of IAU Transactions) and personal communications: 1843: The German amateur astronomer Heinrich Schwabe discovered the 11year cycles of solar activity.…”

“…The Debrecen data helped Moon et al (2016) in developing a new method of image patch analysis of sunspots and active regions. In the studies of the Solar Irradiance Climate Data Record by Coddington et al (2016), the DPD will be used as an independent data source that is accessed for quality assurance of the sunspot darkening.…”

Ensemble Estimation of Information Divergence †

“…Information divergences are integral functionals of two probability distributions and have many applications in the fields of information theory, statistics, signal processing, and machine learning. Some applications of divergences include estimating the decay rates of error probabilities [ 1 ], estimating bounds on the Bayes error [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ] or the minimax error [ 9 ] for a classification problem, extending machine learning algorithms to distributional features [ 10 , 11 , 12 , 13 ], testing the hypothesis that two sets of samples come from the same probability distribution [ 14 ], clustering [ 15 , 16 , 17 ], feature selection and classification [ 18 , 19 , 20 ], blind source separation [ 21 , 22 ], image segmentation [ 23 , 24 , 25 ], and steganography [ 26 ]. For many more applications of divergence measures, see reference [ 27 ].…”