Related papers: Implementation paradigm for supervised flare forec…

Solar flares create adverse space weather impacting space and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single…

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…

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…

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active…

A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we…

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 flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We…

A demonstrated failure mode for operational solar flare forecasting is the inability to forecast flares that occur near, or just beyond, the solar limb. To address this shortcoming, we develop a "4pi" full-heliosphere event forecasting…

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…

Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms.…

Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass ejections are sources…

Based on several magnetic nonpotentiality parameters obtained from the vector photospheric active region magnetograms obtained with the Solar Magnetic Field Telescope at the Huairou Solar Observing Station over two solar cycles, a machine…

Large scale solar eruptions significantly impact space weather and damages space-based human infrastructures. It is necessary to predict large scale solar eruptions, which will enable us to protect our vulnerable infrastructures of modern…

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…

Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning…

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The…

Solar flares are among the most severe space weather phenomena, and they have the capacity to generate radiation storms and radio disruptions on Earth. The accurate prediction of solar flare events remains a significant challenge, requiring…

This study aims to evaluate the performance of deep learning models in predicting $\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing…

Space weather phenomena such as solar flares, have massive destructive power when reaches certain amount of magnitude. Such high magnitude solar flare event can interfere space-earth radio communications and neutralize space-earth…

We developed an operational solar flare prediction model using deep neural networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two categories, such as >=M-class and <M-class events or >=C-class…