Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

• Published in: Solar physics Vol. 293; no. 2; pp. 1 - 42
• Main Authors: Florios, Kostas, Kontogiannis, Ioannis, Park, Sung-Hong, Guerra, Jordan A., Benvenuto, Federico, Bloomfield, D. Shaun, Georgoulis, Manolis K.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2018; Springer Nature B.V
• Subjects: Artificial intelligence; Astrophysics and Astroparticles; Atmospheric Sciences; Computer simulation; Entropy; Forecasting; Machine learning; Monte Carlo simulation; Objective function; Photosphere; Physics; Physics and Astronomy; Solar activity; Solar activity regions; Solar cycle; Solar flares; Solar observatories; Solar physics; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical analysis; Support vector machines; Flares, relation to magnetic field; Flares, forecasting; Active regions, magnetic fields

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out...