Award Abstract # 2104004
Elements: Spatiotemporal Analysis of Magnetic Polarity Inversion Lines (STEAMPIL)

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: GEORGIA STATE UNIVERSITY RESEARCH FOUNDATION INC
Initial Amendment Date: April 6, 2021
Latest Amendment Date: May 21, 2021
Award Number: 2104004
Award Instrument: Standard Grant
Program Manager: Varun Chandola
vchandol@nsf.gov
 (703)292-2656
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 1, 2021
End Date: June 30, 2026 (Estimated)
Total Intended Award Amount: $599,879.00
Total Awarded Amount to Date: $599,879.00
Funds Obligated to Date: FY 2021 = $599,879.00
History of Investigator:
  • Berkay Aydin (Principal Investigator)
    baydin2@gsu.edu
  • Petrus Martens (Co-Principal Investigator)
Recipient Sponsored Research Office: Georgia State University Research Foundation, Inc.
58 EDGEWOOD AVE NE
ATLANTA
GA  US  30303-2921
(404)413-3570
Sponsor Congressional District: 05
Primary Place of Performance: Georgia State University
25 Park Pl NE Suite 700
Atlanta
GA  US  30303-2921
Primary Place of Performance
Congressional District:
05
Unique Entity Identifier (UEI): MNS7B9CVKDN7
Parent UEI:
NSF Program(s): SOLAR-TERRESTRIAL,
Software Institutes,
EarthCube
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 4444, 077Z, 7923, 8004
Program Element Code(s): 152300, 800400, 807400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Extreme space weather events such as solar flares, coronal mass ejections, energetic proton events and geomagnetic storms can cause massive disruptions in many technologically complex systems, including radio communications, telecommunication and navigation satellites, electrical power systems, or space and even commercial airline flights. This project builds a detection and analysis cyberinfrastructure, and investigates one of the most distinctive features in the solar atmosphere ? magnetic polarity inversion lines, which are hotspots of the most intense eruptive activity. Analyzing these features enables solar physicists to advance understanding of extreme space weather events and provide needed predictive capabilities for space weather forecasters.

This project creates an innovative and sustainable software infrastructure to detect, characterize and analyze polarity inversion lines. The first step toward that objective is the identification of polarity inversion lines, and quantitative characterization of these multi-faceted features through image descriptors. In subsequent stages, this project analyzes the time series of these features and descriptors using advanced machine learning and data mining techniques, specifically for improving space weather forecasting capabilities. This includes analyzing the spatiotemporal patterns of emergence and disappearance for polarity inversion lines, selecting and understanding important shape characteristics of these lines pertinent to solar eruptive activity, and creating a prototype eruption forecasting system with discovered precursors. Automatically identifying and analyzing polarity inversion lines has several direct benefits: physically understanding solar magnetic shear layers and the transition from typical non-eruptive active region states to intense, eruptive ones; making contributions to forecasting of solar eruptions; and generating descriptors and measures that can be useful to the study of shear layers in nature.

This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Solar Terrestrial Physics Program and the Division of Integrative and Collaborative Education and Research within the NSF Directorate for Geosciences.

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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(Showing: 1 - 10 of 11)
Hong, Jinsu and Ji, Anli and Pandey, Chetraj and Aydin, Berkay "Beyond Traditional Flare Forecasting: A Data-driven Labeling Approach for High-fidelity Predictions" The 25th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2023) , 2023 Citation Details
Hong, Jinsu and Ji, Anli and Pandey, Chetraj and Aydin, Berkay "Enhancing Solar Flare Prediction with Innovative Data-Driven Labels" , 2023 https://doi.org/10.1109/cogmi58952.2023.00035 Citation Details
Hong, Jinsu and Pandey, Chetraj and Ji, Anli and Aydin, Berkay "An Innovative Solar Flare Metadata Collection for Space Weather Analytics" , 2023 https://doi.org/10.1109/icmla58977.2023.00063 Citation Details
Ji, Anli and Aydin, Berkay "Interpretable Solar Flare Prediction with Sliding Window Multivariate Time Series Forests" , 2023 https://doi.org/10.1109/bigdata59044.2023.10386908 Citation Details
Ji, Anli and Cai, Xumin and Khasayeva, Nigar and Georgoulis, Manolis_K and Martens, Petrus_C and Angryk, Rafal_A and Aydin, Berkay "A Systematic Magnetic Polarity Inversion Line Data Set from SDO/HMI Magnetograms" The Astrophysical Journal Supplement Series , v.265 , 2023 https://doi.org/10.3847/1538-4365/acb43a Citation Details
Pandey, Chetraj and Angryk, Rafal A and Aydin, Berkay "Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions" , 2023 https://doi.org/10.1109/icmla58977.2023.00103 Citation Details
Pandey, Chetraj and Angryk, Rafal A and Georgoulis, Manolis K and Aydin, Berkay "Explainable Deep Learning-Based Solar Flare Prediction with Post Hoc Attention for Operational Forecasting" , 2023 Citation Details
Pandey, Chetraj and Angryk, Rafal and Aydin, Berkay "Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods" European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases , 2023 Citation Details
Pandey, Chetraj and Ji, Anli and Angryk, Rafal A and Aydin, Berkay "Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks" , 2023 https://doi.org/10.1109/aike59827.2023.00021 Citation Details
Pandey, Chetraj and Ji, Anli and Angryk, Rafal A. and Georgoulis, Manolis K. and Aydin, Berkay "Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting" Frontiers in Astronomy and Space Sciences , v.9 , 2022 https://doi.org/10.3389/fspas.2022.897301 Citation Details
Pandey, Chetraj and Ji, Anli and Nandakumar, Trisha and Angryk, Rafal A and Aydin, Berkay "Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations" , 2023 https://doi.org/10.1109/dsaa60987.2023.10302639 Citation Details
(Showing: 1 - 10 of 11)

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