16 citations · 61 across the 6 of their papers we have counts for
6 papers
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…
A Deep Learning Approach to Dst Index Prediction
Yasser Abduallah, Jason T. L. Wang, Prianka Bose +3
The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic st…
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…
Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations
Gelu Nita, Azim Ahmadzadeh, Serena Criscuoli +15
Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high-cadence, high-resolution multiwavelength observations, growing amounts of dat…
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…