7 papers
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
Sathvik Soman, Jason T. L. Wang, Haimin Wang +1
We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar f…
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…
Out-of-Sample Validation of MagNet
Aryiadna Yesmanchyk, Yan Xu, Jason T. L. Wang +3
Machine learning is starting to be used in almost every industry and academic research, and solar physics is no exception. A newly developed machine learning model named MagNet hel…
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…
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions
Huseyin Cavus, Jason T. L. Wang, Teja P. S. Singampalli +4
Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. So…