85 citations · 178 across the 11 of their papers we have counts for
8 papers · 1 filter
Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
Khalid A. Alobaid, Yasser Abduallah, Jason T. L. Wang +5
Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two propert…
Ensemble Learning for CME Arrival Time Prediction
Khalid A. Alobaid, Jason T. L. Wang
The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry a…
A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
Haodi Jiang, Qin Li, Zhihang Hu +8
Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have bee…
Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Haodi Jiang, Qin Li, Yan Xu +5
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep N…
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
Hewei Zhang, Qin Li, Yanxing Yang +4
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can…
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Yasser Abduallah, Vania K. Jordanova, Hao Liu +3
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. In this paper we propose…
Tracing Halpha Fibrils through Bayesian Deep Learning
Haodi Jiang, Ju Jing, Jiasheng Wang +5
We present a new deep learning method, dubbed FibrilNet, for tracing chromospheric fibrils in Halpha images of solar observations. Our method consists of a data pre-processing comp…
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
Haodi Jiang, Jiasheng Wang, Chang Liu +4
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propo…