Machine Learning Study Identifies the Internal Structure of
Interplanetary Coronal Mass Ejections
In a machine learning (ML) study supported by the NASA Center for Climate Simulation (NCCS), NASA Goddard Space Flight Center and university heliophysics researchers have gained insights into the internal structure of interplanetary coronal mass ejections (ICMEs)—gigantic clouds of magnetized gas that erupt from the Sun and travel through the solar system.
Study results were published in the journal Solar Physics and presented at the virtual 2020 American Geophysical Union (AGU) Fall Meeting in December by Luiz Fernando Guedes dos Santos, research assistant in NASA Goddard’s Heliospheric Physics Laboratory and a PhD candidate in the Department of Physics at the Catholic University of America.
ICMEs are the main drivers of space weather that can damage satellites and power grids if they hit Earth’s magnetosphere (protective magnetic field) in the right locations. From observations by NASA’s Wind spacecraft, scientists hypothesize that ICMEs consist of flux ropes, bundles of twisted magnetic field lines that wrap around a common axis into tubes (see images below).
NASA Wind mission data played important roles in the ML study, which took the form of a deep neural network using both observed (Wind) and synthetic (physics-based and empirical model) image data to “train” the computer to recognize a wide variety of ICME flux rope structures. The researchers leveraged a proven handwriting recognition model for the neural network and designed the study data to be compatible with it.
The neural network model then ran on a Heliospheric Physics Laboratory GPU system. The model was able to identify flux rope signatures with high accuracy when comparing the results to a catalog of 353 Wind ICME observations from 1995–2015 (see plots below). It correctly classified 84% of simple real cases and 76% of a broader set of cases.
After analyzing the discrepancies between the catalog and ML-based classification, the team continues discussing whether some events should be reclassified and how the classification criteria could be improved for future studies. The team also notes the need to further develop their flux rope models to include more observed features.
“Having access to NASA supercomputing resources enabled us to focus on the science and programming,” dos Santos said. “This was essential to give us peace of mind that we had all the computing power we needed to produce important scientific advances.”
- dos Santos, L.F., A. Narock, T. Nieves-Chinchilla, M. Nuñez, and M. Kirk, 2020: Identifying Flux Rope Signatures Using a Deep Neural Network. Solar Physics, 295, no. 10, 131,
- NG004-0024 - Identifying Flux Rope Signatures Using a Deep Neural Network, Poster, 2020 American Geophysical Union Fall Meeting, 12/15/20 (registration required).
Jarrett Cohen, NASA Goddard Space Flight Center