ANSWERS: Prediction of Geoeffective Solar Eruptions, Geomagnetic Indices, and Thermospheric Density Using Machine Learning Methods
Project Summary
Understanding and predicting eruptions on the Sun
and their terrestrial impacts are a research
as well as strategic national priority,
as such space weather affects our electronic communication,
electric power supply, satellite infrastructure,
national defense, and more.
This project is a collaboration among
Rutgers University, New Jersey Institute of Technology,
West Virginia University, and Montclair State University
that will improve our ability to predict several
linked space weather components:
geoeffective solar eruptions,
the global magnetic response of Earth to these eruptions,
as well as variation of neutral density
in the Earth's thermosphere
and its effect on satellite drag.
The work covers many aspects of geospace science,
solar physics, and data science including machine learning.
The innovative machine learning tools developed from the project
will be applicable for analyzing disparate data sets
in astronomy and other areas of science.
The two key science questions are:
What are the physical mechanisms for the onset of geoeffective
solar eruptions?
And what are the effects of solar eruptions
on neutral density in the thermosphere?
Specifically, the project will create synthetic vector magnetograms
using ground- and space-based data
for solar cycles 23 and 24;
develop machine learning (ML) tools to predict
solar flares and associated geoeffective coronal mass ejections (CMEs)
based on magnetogram parameters;
predict geomagnetic indices from derived magnetic properties
of solar active regions and CMEs,
solar wind parameters and solar images;
and predict neutral density in the thermosphere
using ML approaches that integrate satellite data,
observed and predicted geomagnetic indices,
and empirical neutral density models.
Support
This material is based upon work partly supported
by the United States National Science Foundation under grant
AGS-2149748
(2022-2026).
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the authors and do not necessarily
reflect the views of the National Science Foundation.
This support is greatly appreciated.