Related papers: A Bayesian Approach to Solar Flare Prediction
The Bayesian approach to the prediction of particle type given measurements of particle location is explored, using a parametric model whose prior is based on the transformation group. Two types of particle are considered, and locations are…
The ability to predict the occurrence of solar flares in advance is important to humankind due to the potential damage they can cause to Earth's environment and infrastructure. It has been shown in Kusano et al. (2012) that a small-scale…
Understanding the physical processes that trigger solar flares is paramount to help with forecasting space weather and mitigating the effects on our technological infrastructure. A previously unknown phenomenon was recently identified in…
We present a study of multi-wavelength observations, of a C 2.3 solar flare in Active Region NOAA 12353, observed on 2015 May 23, which reveal new properties of acoustic waves in the flaring region. The space-, and ground-based data…
Strong solar flares and coronal mass ejections, here defined not only as the bursts of electromagnetic radiation but as the entire process in which magnetic energy is released through magnetic reconnection and plasma instability, emanate…
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…
A phenomenological model is presented for the quantitative description of individual solar cycles' features, such as onset, intensity, evolution, in terms of the number of M and X-class solar flares. The main elements of the model are the…
AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per la…
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…
In order to understand the flare trigger mechanism, we conducted three-dimensional magnetohydrodynamic simulations using a coronal magnetic field model derived from data observed by the Hinode satellite. Several types of magnetic bipoles…
We used multi-wavelength high-resolution data from ARIES, THEMIS, and SDO instruments, to analyze a non-standard, C3.3 class flare produced within the active region NOAA 11589 on 2012 October 16. Magnetic flux emergence and cancellation…
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…
Solar flares are the most powerful explosions occurring in the solar system, which may lead to disastrous space weather events and impact various aspects of our Earth. So far, it is still a big challenge in modern astrophysics to understand…
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…
We report the results of a high temporal resolution spectroscopic monitoring of the flare star AD Leo. During 4 nights, more than 600 spectra were taken in the optical range using the Isaac Newton Telescope (INT) and the Intermediate…
Every 11 years, the Sun goes through periods of activity, with the occurrence of many solar flares and mass ejections, both energetic phenomena of magnetic origin. Due to its effects on Earth, the study of solar activity is of paramount…
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…
There is a wide range of eruptions in the solar atmosphere which contribute to space weather, including the major explosions of radiation known as flares. To examine pre-event behavior in delta-spot regions, we use here a method based on…
This study explores the behavior of machine learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University's Space Weather Analytics for Solar Flares benchmark dataset (Angryk et al.…
We present a new automated algorithm to identify, track, and characterize small-scale brightening associated with solar eruptive phenomena observed in H{\alpha}. The temporal spatially-localized changes in chromospheric intensities can be…