相关论文: On flare predictability based on sunspot group evo…

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…

Solar flares produce radiation which can have an almost immediate effect on the near-Earth environment, making it crucial to forecast flares in order to mitigate their negative effects. The number of published approaches to flare…

Solar active regions (ARs) that produce strong flares and coronal mass ejections (CMEs) are known to have a relatively high non-potentiality and are characterized by delta-sunspots and sheared magnetic structures. In this study, we conduct…

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can severely impact the near-Earth environment. Thus it is…

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…

Large and magnetically complex sunspot groups are known to be associated with flares. To date, the Mount Wilson scheme has been used to classify sunspot groups based on their morphological and magnetic properties. The most flare prolific…

The aim of the present investigation is to study the spatio-temporal distribution of precursor flares during the 24-hour interval preceding M- and X-class major flares and the evolution of follower flares. Information on associated…

A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $\Gamma$. We considered $\Gamma$ are $\ge M$,…

This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond…

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 flares are events of intense scientific interest. Although certain solar conditions are known to be associated with flare activity, the exact location and timing of an individual flare on the Sun cannot as yet be predicted with…

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…

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…

Flares from the supermassive black hole in our Galaxy, Sagittarius~A$^\star$ (Sgr A$^\star$), are routinely observed over the last decade or so. Despite numerous observational and theoretical efforts, the nature of such flares still remains…

Accurate and reliable predictions of solar flares are essential due to their potentially significant impact on Earth and space-based infrastructure. Although deep learning models have shown notable predictive capabilities in this domain,…

We use Renewal Theory for the estimation and interpretation of the flare rate from the Geostationary Operational Environmental Satellite (GOES) soft X-ray flare catalogue. It is found, that in addition to the flare rate variability with the…

Solar flares and coronal mass ejections (CMEs), especially the larger ones, emanate from active regions (ARs). With the aim to understand the magnetic properties that govern such flares and eruptions, we systematically survey all flare…

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 study focuses on forecasting major (>=M-class) solar flares that can severely impact the near-Earth environment. We construct two types of datasets using the Space Weather HMI Active Region Patches (SHARP), and develop a flare…

A convolutional neural network (CNN) is used to construct a new catalog for solar flares based on high resolution (1-s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data. The CNN is trained to identify flare…