Related papers: Toward Reliable Benchmarking of Solar Flare Foreca…
Solar flares not only pose risks to outer space technologies and astronauts' well being, but also cause disruptions on earth to our hight-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning…
The newly revised sunspot number series allows for placing historical geoeffective storms in the context of several hundred years of solar activity. Using statistical analyses of the Geostationary Operational Environmental Satellites (GOES)…
Solar flares commonly have a hot onset precursor event" (HOPE), detectable from soft X-ray observations. Detecting this requires subtraction of pre-flare fluxes from the non-flaring Sun prior to the event, fitting an isothermal emission…
We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6,160 different combinations of SDO/HMI data as input data, and simulated the prediction of GOES X-ray flux for 2…
Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors including source eruptions. In the present work, we…
Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecasting. In this work, we…
The spot evolution on the Sun and solar-type stars is important for understanding the nature of consequential flaring activity. This study statistically investigates the variance of flare occurrence rate through the time evolution of spots…
A statistical analysis of magnetic energies of the nonlinear force-free and potential fields, and their difference (a proxy for the free magnetic energy) in active regions (ARs) on the Sun of different Hale (Mount Wilson) and McIntosh…
In this paper, we introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches and provide a novel capability for predicting solar flares covering the entirety of the solar disk (AR…
This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative…
Flare ribbons are always dynamic, and sometimes sweep across sunspots. Examining 588 (513 M-class and 75 X-class) flare events observed by Transition Region and Coronal Explorer (TRACE) satellite and Hinode Solar Optical Telescope (SOT)…
High energy solar flares and coronal mass ejections have the potential to destroy Earth's ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering. Destruction of these critical systems would…
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of…
The solar X-ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. As the duration of a solar flare is not related to the size of that…
The solar activity in the current, that is, the 24-th, sunspot cycle is analyzed. Cyclic variations in the sunspot number (SSN) and radiation fluxes in various spectral ranges have been estimated in comparison with the general level of the…
We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument…
Post-flare arcades are well-known components of solar flare evolution, which have been observed for several decades. Coronal rain, cascades of catastrophically-cooled plasma, outline the loops and provide eye-catching evidence of the recent…
The prediction of solar activity is important for advanced technologies and space activities. The peak sunspot number (SSN), which can represent the solar activity, has declined continuously in the past four solar cycles (21$-$24), and the…
The flare activity and the ultraviolet emission of the sun during its 24-th cycle are analysed. As compared to cycles 21-23, where the most powerful flares were observed during the decay phase, in cycle 24 the greatest number of powerful…
Recently, there has been growing interest in the use of machine-learning methods for predicting solar flares. Initial efforts along these lines employed comparatively simple models, correlating features extracted from observations of…