
NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
Recipient: |
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Initial Amendment Date: | April 22, 2022 |
Latest Amendment Date: | December 13, 2023 |
Award Number: | 2149747 |
Award Instrument: | Standard Grant |
Program Manager: |
Mangala Sharma
msharma@nsf.gov (703)292-4773 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
Start Date: | May 1, 2022 |
End Date: | April 30, 2026 (Estimated) |
Total Intended Award Amount: | $539,988.00 |
Total Awarded Amount to Date: | $596,791.00 |
Funds Obligated to Date: |
FY 2024 = $56,803.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
Sponsor Congressional District: |
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Primary Place of Performance: |
33 Knightsbridge Road Piscataway NJ US 08854-3925 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
MAGNETOSPHERIC PHYSICS, AERONOMY, Space Weather Research |
Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.050 |
ABSTRACT
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. Faculty members, early career researchers including a postdoctoral fellow and graduate students will collaborate on the project, creating a multidisciplinary training program for future generations of scientists. The project will emphasize diversity and the participation of underrepresented minorities through both the research efforts and education activities such as K-12 teacher workshops.
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. Most of the funding will be used to support three graduate students (one at WVU and two at NJIT) and a postdoc at Rutgers. K-12 teacher workshops will be organized by Montclair State University. ANSWERS projects advance the nation?s STEM expertise and societal resilience to space weather hazards by filling key knowledge gaps regarding the coupled Sun-Earth system.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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