8 papers
Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations
Zhaoxin Yan, Jason T. L. Wang, Haimin Wang +5
Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space environment and on Earth. In th…
Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang +6
The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable…
Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network
Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang +6
Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The associat…
Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Haodi Jiang, Qin Li, Jason T. L. Wang +2
Solar extreme ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time sc…
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang +6
We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michel…
Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model
Hongyang Zhang, Ju Jing, Jason T. L. Wang +6
We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where…