Related papers: Complex Network for Solar Active Regions

Long-lasting activity complexes (ACs), characterised as a series of closely located, continuously emerging solar active regions (ARs), are considered generating prominent poleward surges from observations. The surges lead to significant…

Solar flares emanate from solar active regions hosting complex and strong bipolar magnetic fluxes. Estimating the probability of an active region to flare and defining reliable precursors of intense flares is an extremely challenging task…

We used line-of-sight magnetograms acquired by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory to derive the decay rate of total unsigned magnetic flux for 910 ephemeral and active regions (ARs) observed between…

Activity or spin patterns on random scale-free network are studied by mean field analysis and computer simulations. These activity patterns evolve in time according to local majority-rule dynamics which is implemented using (i) parallel or…

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine…

In this letter, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called {\bf Random Apollonian Networks}(RANs) as they can be considered…

This study proposes an enhancement to the existing method for detecting Solar Active Regions (ARs). Our technique tracks ARs using images from the Atmospheric Imaging Assembly (AIA) of NASA's Solar Dynamics Observatory (SDO). It involves a…

Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in…

The question of the nature of precursors of solar flares, as well as their relationship with subsequent flares, still has no unambiguous answer. This is due, in particular, to the lack of systematic statistical work, the relative…

We used multi-wavelength high-resolution data from ARIES, THEMIS, and SDO instruments, to analyze a non-standard, C3.3 class flare produced within the active region NOAA 11589 on 2012 October 16. Magnetic flux emergence and cancellation…

We present a study of multi-wavelength observations, of a C 2.3 solar flare in Active Region NOAA 12353, observed on 2015 May 23, which reveal new properties of acoustic waves in the flaring region. The space-, and ground-based data…

Recent studies indicate that measurements of fractal and multifractal parameters of active regions (ARs) are not efficient tools to discriminate ARs on the basis of the flare activity, as well as to predict flare events. Attempting…

We study the properties of 2D fibre clusters and networks formed by deposition processes. We first examine the growth and scaling properties of single clusters. We then consider a network of such clusters, whose spatial distribution obeys…

There is no list of bipolar active regions (ARs) with reverse polarity (anti-Hale regions), although statistical investigations of such ARs (bearing the imprint of deep subphotospheric processes) are important for understanding solar-cycle…

The observed powerlaw distributions of solar flare parameters can be interpreted in terms of a nonlinear dissipative system in the state of self-organized criticality (SOC). We present a universal analytical model of a SOC process that is…

Context. In September 2017 the largest X-class flare of Solar Cycle 24 occurred from the most active region (AR) of this cycle, AR 12673. The AR attracted much interest because of its unique morphological and evolution characteristics.…

Solar Cycle 24 data are used to determine how often the Sun emerges sunspots in `activity nests', i.e., regions where sunspots and active regions (ARs) repeatedly emerge. We use the Solar Photospheric Ephemeral Active Region (SPEAR) catalog…

With machine learning entering into the awareness of the heliophysics community, solar flare prediction has become a topic of increased interest. Although machine learning models have advanced with each successive publication, the input…

The eigenvalues and eigenvectors of the connectivity matrix of complex networks contain information about its topology and its collective behavior. In particular, the spectral density $\rho(\lambda)$ of this matrix reveals important network…

Solar active regions (ARs) determine solar polar fields and cause solar cycle variability within the framework of the Babcock-Leighton (BL) dynamo. The contribution of an AR to the polar field is measured by its dipole field, which results…