Related papers: A Deep Learning Approach to Operational Flare Fore…
Many questions must be answered before understanding the relationship between the emerging magnetic flux through the solar surface and the extreme geoeffective events. The main ingredients for getting X-ray class flares and large…
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the…
Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast…
The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An…
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…
We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent…
Data assimilation techniques, developed in the last two decades mainly for weather prediction, produce better forecasts by taking advantage of both theoretical/numerical models and real-time observations. In this paper, we explore the…
Solar flares are energetic events taking place in the Sun's atmosphere, and their effects can greatly impact the environment of the surrounding planets. In particular, eruptive flares, as opposed to confined flares, launch coronal mass…
Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar…
We studied the soft X-ray data of solar flares and found that the distribution functions of flare fluence are successfully modeled by tapered power law or gamma function distributions whose power exponent is slightly smaller than 2,…
We study the photospheric magnetic field of ~2000 active regions in solar cycle 23 to search for parameters indicative of energy build-up and subsequent release as a solar flare. We extract three sets of parameters: snapshots in space and…
Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I--V) based on their…
We establish the largest eruptive/confined flare database to date and analyze 322 flares of \emph{GOES} class M1.0 and larger that occurred during 2010$-$2019, i.e., almost spanning the entire solar cycle 24. We find that the total unsigned…
We study the energy release process of a set of 51 flares (32 confined, 19 eruptive) ranging from GOES class B3 to X17. We use H$\alpha$ filtergrams from Kanzelh\"ohe Observatory together with SDO HMI and SOHO MDI magnetograms to derive…
In this study we determine scaling relationships of observed solar flares that can be used to predict upper limits of the GOES-class magnitude of solar flares. The flare prediction scheme is based on the scaling of the slowly-varying…
In this paper, we present the multi-wavelength study of a high level of solar activity during which a single active region produced multiple flares/CMEs. According to the sunspot observations, the current solar cycle 24 manifest to be less…
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal…
The prediction of solar flares is of practical and scientific interest; however, many machine learning methods used for this prediction task do not provide the physical explanations behind a model's performance. We made use of two recently…
Solar eruptive events, like flares and coronal mass ejections, are characterized by the rapid release of energy that can give rise to emission of radiation across the entire electromagnetic spectrum and to an abrupt significant increase in…
Solar flares are sudden and violent releases of magnetic energy in the solar atmosphere that can be divided in eruptive flares, when plasma is ejected from the solar atmosphere, resulting in a coronal mass ejection (CME), and confined…