Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

• Published in: The Astrophysical journal Vol. 856; no. 1; pp. 7 - 17
• Main Authors: Huang, Xin, Wang, Huaning, Xu, Long, Liu, Jinfu, Li, Rong, Dai, Xinghua
• Format: Journal Article
• Language: English
• Published: Philadelphia The American Astronomical Society 20-03-2018; IOP Publishing
• Subjects: Astrophysics; Deep learning; Forecasting; Forecasting techniques; Line of sight; Machine learning; Magnetic fields; Mathematical models; methods: data analysis; Model testing; Polarity; Soft x rays; Solar activity; Solar activity regions; Solar flare forecasting; Solar flares; Solar magnetic field; Sun: activity; Sun: flares; techniques: image processing
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/aaae00

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.