Related papers: Predicting Solar Flares Using SDO/HMI Vector Magne…

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…

Monitoring of the Sun and its activity is a task of growing importance in the frame of space weather research and awareness. Major space weather disturbances at Earth have their origin in energetic outbursts from the Sun: solar flares,…

Solar flare forecasting mainly relies on photospheric magnetograms and associated physical features to predict forthcoming flares. However, it is believed that flare initiation mechanisms often originate in the chromosphere and the lower…

The prediction of solar flares is still a significant challenge in space weather research, with no techniques currently capable of producing reliable forecasts performing significantly above climatology. In this paper, we present a flare…

Efficient prediction of solar flares relies on parameters that quantify the eruptive capability of solar active regions. Several such quantitative predictors have been proposed in the literature, inferred mostly from photospheric…

In this work we leverage a weakly-labeled dataset of spectral data from NASAs IRIS satellite for the prediction of solar flares using the Multiple Instance Learning (MIL) paradigm. While standard supervised learning models expect a label…

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…

Improving the performance of solar flare forecasting is a hot topic in solar physics research field. Deep learning has been considered a promising approach to perform solar flare forecasting in recent years. We first used the Generative…

Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often…

Multi--wavelength studies of energetic solar flares with seismic emissions have revealed interesting common features between them. We studied the first GOES X--class flare of the 24th solar cycle, as detected by the Solar Dynamics…

Of all the activity observed on the Sun, two of the most energetic events are flares and Coronal Mass Ejections (CMEs). Usually, solar active regions that produce large flares will also produce a CME, but this is not always true (Yashiro et…

The paper presents results of a search for helioseismic events (sunquakes) produced by M-X class solar flares during Solar Cycle 24. The search is performed by analyzing photospheric Dopplergrams from Helioseismic Magnetic Imager (HMI).…

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…

A magnetic power spectral analysis is performed on 53 solar active regions, observed from August 2011 to July 2012. Magnetic field data obtained from the Helioseismic and Magnetic Imager, inverted as Active Region Patches, are used to study…

We present analysis of the magnetic field in seven solar flare regions accompanied by the pulsations of hard X-ray (HXR) emission. These flares were studied by Kuznetsov et al. (2016) (Paper~I), and chosen here because of the availability…

Machine learning models for forecasting solar flares have been trained and evaluated using a variety of data sources, including Space Weather Prediction Center (SWPC) operational and science-quality data. Typically, data from these sources…

Solar flares represent one of the most intense forms of solar activity. Understanding the evolution of physical parameters in the solar atmosphere during flares is key to studying flare mechanisms and improving prediction capabilities.…

We investigate the use of Long Short-Term Memory (LSTM) and Decomposition-LSTM (DLSTM) networks, combined with an ensemble algorithm, to predict solar flare occurrences using time-series data from the GOES catalog. The dataset spans from…

Observational pre-cursors of large solar flares provide a basis for future operational systems for forecasting. Here, we study the evolution of the normalized emergence (EM), shearing (SH) and total (T) magnetic helicity flux components for…

This study identifies the solar origins of magnetic clouds that are observed at 1 AU and predicts the helical handedness of these clouds from the solar surface magnetic fields. We started with the magnetic clouds listed by the Magnetic…