5 papers
Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning
Yash Chaudhary, Jason T. L. Wang, Chunhui Xu +2
Solar flares, particularly those of the M- and X-class, have a significant impact on human life because of their potential to disrupt critical infrastructure and communication syst…
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…
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…
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…
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Chunhui Xu, Jason T. L. Wang, Haimin Wang +4
Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image sup…