Related papers: Flare Forecasting Using the Evolution of McIntosh …
Aims. In this paper, we investigate the temporal evolution and north-south (N-S) asymmetry in the occurrence of solar flares during cycle 21, 22, and 23, and compare the results with traditional solar activity indices. Methods. The flare…
Solar flares are intense eruptive events caused by the rapid release of magnetic energy, often impacting Earth's space environment through electromagnetic radiation and high-energy particles. Accurate flare prediction is critical for space…
Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth.…
Sunspots are regions of decreased brightness on the visible surface of the Sun (photosphere) that are associated with strong magnetic fields. They have been found to be locations associated with solar flares, which occur when energy stored…
Solar active regions and the processes that occur in them have been extensively studied and analyzed and many types of models and characterizations have been proposed for the occurrence of different eruptive events that take place in the…
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…
We apply a novel pre-flare tracking of sunspot groups towards improving the estimation of flare onset time by focusing on the evolution of the 3D magnetic field construction of AR 11429. The 3D magnetic structure is based on potential field…
A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe…
Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun's atmosphere. These energetic bursts of electromagnetic radiation can release up to…
We developed a reliable probabilistic solar flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur in the following 24 h after observing…
It was recently pointed out that the distribution of times between solar flares (the flare waiting-time distribution) follows a power law, for long waiting times. Based on 25 years of soft X-ray flares observed by Geostationary Operational…
A test of a new Bayesian approach to solar flare prediction (Wheatland 2004a) is presented. The approach uses the past history of flaring together with phenomenological rules of flare statistics to make a prediction for the probability of…
Adverse space weather effects can often be traced to solar flares, prediction of which has drawn significant research interests. The Helioseismic and Magnetic Imager (HMI) produces full-disk vector magnetograms with continuous high cadence,…
Solar flare forecasting research using machine learning (ML) has focused on high resolution magnetogram data from the SDO/HMI era covering Solar Cycle 24 and the start of Solar Cycle 25, with some efforts looking back to SOHO/MDI for data…
Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and the topology of solar magnetic fields. A new method for predicting large (M and X class) flares is…
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…
Traditional solar flare forecasting approaches have mostly relied on physics-based or data-driven models using solar magnetograms, treating flare predictions as a point-in-time classification problem. This approach has limitations,…
We conduct a post hoc analysis of solar flare predictions made by a Long Short Term Memory (LSTM) model employing data in the form of Space-weather HMI Active Region Patches (SHARP) parameters calculated from data in proximity to the…
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours. Using…
Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to…