608 citations · 1.2k across the 10 of their papers we have counts for
10 papers
The Coronal Global Evolutionary Model: Using HMI Vector Magnetogram and Doppler Data to Determine Coronal Magnetic Field Evolution
J. Todd Hoeksema, William P. Abbett, David J. Bercik +10
The Coronal Global Evolutionary Model (CGEM) provides data-driven simulations of the magnetic field in the solar corona to better understand the build-up of magnetic energy that le…
Predicting solar flares with machine learning: investigating solar cycle dependence
Xiantong Wang, Yang Chen, Gabor Toth +7
A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 ho…
A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
Richard Galvez, David F. Fouhey, Meng Jin +8
In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scienti…
Electric-Current Neutralization, Magnetic Shear, and Eruptive Activity in Solar Active Regions
Yang Liu, Xudong Sun, Tibor Török +2
The physical conditions that determine whether or not solar active regions (ARs) produce strong flares and coronal mass ejections (CMEs) are not yet well understood. Here we invest…
Magnetic Helicity Estimations in Models and Observations of the Solar Magnetic Field. Part III: Twist Number Method
Y. Guo, E. Pariat, G. Valori +7
We study the writhe, twist and magnetic helicity of different magnetic flux ropes, based on models of the solar coronal magnetic field structure. These include an analytical force-…
The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance
J. Todd Hoeksema, Yang Liu, Keiji Hayashi +9
The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipel…