1 code implementation • 7 Apr 2022 • Zeyu Sun, Monica G. Bobra, Xiantong Wang, Yu Wang, Hu Sun, Tamas Gombosi, Yang Chen, Alfred Hero
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.
1 code implementation • 27 Dec 2019 • Hu Sun, Ward Manchester, Zhenbang Jiao, Xiantong Wang, Yang Chen
The dynamics of these parameters have a highly uniform trajectory for many events whose LSTM prediction scores for M/X class flares transition from very low to very high.
1 code implementation • 12 Dec 2019 • Zhenbang Jiao, Hu Sun, Xiantong Wang, Ward Manchester, Tamas Gombosi, Alfred Hero, Yang Chen
We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP).
Solar and Stellar Astrophysics
1 code implementation • 1 Dec 2019 • Xiantong Wang, Yang Chen, Gabor Toth, Ward B. Manchester, Tamas I. Gombosi, Alfred O. Hero, Zhenbang Jiao, Hu Sun, Meng Jin, Yang Liu
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 $\Gamma$.
Solar and Stellar Astrophysics