SH020
Prediction of Solar Transient Events: Data-Driven, Physics-Based, and Hybrid Approaches
Session ID#: 252604
Session Description:
Reliable prediction of solar transient events – such as flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events – remains a critical challenge with both scientific and operational implications. This session invites contributions that advance the state of solar and heliospheric event forecasting using a diverse range of approaches, including but not limited to data-driven models (e.g., machine learning), physics-based models (e.g., MHD simulations), and analytic or hybrid techniques. The main goal of the session is to foster cross-disciplinary discussions on how these complementary methods contribute to a deeper understanding and improved prediction of solar transients. Topics of interest include but not limited to: 1) modeling, development and application of forecasting techniques, 2) data preparation, curation, and augmentation strategies, 3) use of novel observations or multi-modal data products, 4) uncertainty quantification, 5) interpretability and physical insight gained from predictive models, and 6) pathways toward operational implementation.
Index Terms:
2101 Coronal mass ejections [INTERPLANETARY PHYSICS]
2114 Energetic particles [INTERPLANETARY PHYSICS]
7519 Flares [SOLAR PHYSICS, ASTROPHYSICS, AND ASTRONOMY]
7924 Forecasting [SPACE WEATHER]
Primary Convener: Talwinder Singh, Georgia State University, Atlanta, GA, United States
Conveners: Berkay Aydin, Georgia State University, Computer Science, Atlanta, United States and Viacheslav M Sadykov, Georgia State University, Atlanta, GA, United States
Student/Early Career Convener: Sahanaj Banu, University of New Hampshire, Durham, United States
Flare Clustering in Solar Magnetic Activity Cycles (1866017)
Elmer Alberto Ccopa Rivera, Andrews University, Berrien Springs, Michigan, United States, Jay Johnson, Andrews University, Berrien Springs, United States, Jonathan Homan, University of Colorado, Boulder, United States and Simon Wing, The Johns Hopkins University Applied Physics Laboratory, Laurel, United States
Variation in CME Structures and Shock-Sheath Characteristics Across ±35° from the Sun–Earth Line: Insights from STEREO-A and L1 (1870695)
Sahanaj Banu1, Noé Lugaz1, Bin Zhuang2, Nada A Al-Haddad1, Charles J Farrugia1 and Antoinette Broe Galvin1, (1)University of New Hampshire, Durham, United States, (2)Space Science Center, UNH, Durham, United States
Accounting for uncertainties in CME parameters at Earth through data-informed global MHD ensemble simulations (1882806)
Elena Provornikova1, Viacheslav G Merkin1, Eric Winter2, Jeffrey Garretson3, Evangelos Paouris1, Christina Kay2, Angelos Vourlidas2 and Charles Nickolos Arge4, (1)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)Johns Hopkins University Applied Physics Laboratory, Laurel, United States, (3)The Johns Hopkins University, APL, Laurel, MD, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States
The Next Level in AI/ML Forecasting of Solar Transient Events (Invited) (1901815)
Manolis K Georgoulis1, Azim Ahmadzadeh2, Anli Ji3, Chetraj Pandey4, Sumanth Rotti5, Dustin Kempton6, Rafal Angryk4 and Berkay Aydin7, (1)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)University of Missouri St Louis, Department of Computer Science, St Louis, United States, (3)California State University Fullerton, Computer Science, Fullerton, United States, (4)Georgia State University, Computer Science, Atlanta, GA, United States, (5)Aryabhatta Research Institute of Observational Sciences (ARIES), Nainatal, India, (6)Georgia State University, Atlanta, United States, (7)Georgia State University, Computer Science, Atlanta, United States
The Role of Flare Ribbon and 3D Magnetic Field Topology in Flare Eruptivity (1914274)
Tamima Saba, Georgia State University, Physics and Astronomy, Atlanta, GA, United States, Viacheslav M Sadykov, Georgia State University, Atlanta, GA, United States, Griffin Goodwin, Georgia State University, Physics & Astronomy, Atlanta, GA, United States and Piet Martens, Georgia State University, Physics and Astronomy, Atlanta, United States
Data-Based MHD Simulation of a Flare Eruption in AR 13663 Associated with a tilted CME (1914985)
Nian Liu1, Satoshi Inoue2, Ju Jing3, Keiji Hayashi4 and Haimin Wang2, (1)New Jersey Institute of Technology, Edison, NJ, United States, (2)New Jersey Institute of Technology, Center for Solar-Terrestrial Research, Newark, United States, (3)New Jersey Institute of Technology - Institute for Space Weather Sciences, Newark, United States, (4)New Jersey Institute of Technology, Newark, United States
Explaining Solar Flare Predictions with Post Hoc Attribution and Physical Feature Relevance (1916716)
Chetraj Pandey1,2, Jinsu Hong1, Anli Ji3,4, Manolis K Georgoulis5 and Berkay Aydin6, (1)Georgia State University, Atlanta, GA, United States, (2)Texas Christian University, Fort Worth, United States, (3)Georgia State University, Computer Science, Atlanta, GA, United States, (4)California State University Fullerton, Computer Science, Fullerton, United States, (5)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (6)Georgia State University, Computer Science, Atlanta, United States
An AI-powered Surface Flux Transport model to measure high-resolution velocity fields and forecast magnetic flux emergence (1927547)
Dr. Shea A. Hess Webber, PhD1, Nina Bonaventura2, Nishu Karna3, Spiridon Kasapis4, Plinio Guzmán5, Andrés Muñoz-Jaramillo6, Katherine Keegan7, Bibhuti Kumar Jha6 and Anthony Yeates8, (1)Stanford University, HEPL, Stanford, CA, United States, (2)University of Arizona, Steward Observatory, Tucson, United States, (3)Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, United States, (4)Princeton University, Department of Astrophysical Sciences, Princeton, United States, (5)Fused Labs, Inc., Brooklyn, United States, (6)Southwest Research Institute Boulder, Boulder, United States, (7)Emory University, Department of Mathematics, Atlanta, United States, (8)University of Durham, Durham, United Kingdom
Enhancing Explainability in Solar Energetic Particle Event Prediction: A Counterfactual and Feature Mapping Approach (1928485)
Anli Ji1,2, Pranjal Patil3, Chetraj Pandey1,4, Manolis K Georgoulis5 and Berkay Aydin3, (1)Georgia State University, Computer Science, Atlanta, GA, United States, (2)California State University Fullerton, Computer Science, Fullerton, United States, (3)Georgia State University, Computer Science, Atlanta, United States, (4)Texas Christian University, Fort Worth, United States, (5)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
Evaluating the Effectiveness of Active Region/Polarity Inversion Line Segmentation with Heliophysics Foundation Models (1934031)
Jinsu Hong1, Chetraj Pandey2, Kang Yang3, Viacheslav M Sadykov1, Vishal Gaur4, Rohit Lal4, Johannes Schmude5, Andrés Muñoz-Jaramillo6, Sujit Roy7, Talwinder Singh1, Berkay Aydin8 and Rahul Ramachandran9, (1)Georgia State University, Atlanta, GA, United States, (2)Georgia State University, Computer Science, Atlanta, GA, United States, (3)Georgia State University, Atlanta, United States, (4)University of Alabama in Huntsville, Huntsville, United States, (5)IBM Research, Yorktown Heights, United States, (6)Montana State University, Bozeman, United States, (7)NASA Marshall Space Flight Center, Huntsville, United States, (8)Georgia State University, Computer Science, Atlanta, United States, (9)NASA Marshall Space Flight Center, Huntsville, AL, United States
Comparison of Time-dependent Boundary-driven Heliospheric MHD models (1950717)
Tinatin Baratashvili, Katholieke Universiteit Leuven, Centre for mathematical Plasma-Astrophysics (CmPA), Leuven, Belgium, Ronald M. Caplan, Predictive Science Inc., San Diego, United States, Evangelia Samara, Catholic University of America, Washington, United States and Talwinder Singh, Georgia State University, Atlanta, GA, United States
Machine Learning-Driven Solar Flare Nowcasting for the 2026 Sounding Rocket Campaign (1956877)
Marianne S Peterson1, Matthew Choquette1, Lindsay Glesener1, Juan Camilo Buitrago-Casas2, Juliana Therese Vievering3, Andrea Lizeth Lopez Rodriguez4 and Jarvis Haupt1, (1)University of Minnesota Twin Cities, Minneapolis, United States, (2)Space Sciences Laboratory, Berkeley, United States, (3)Applied Physics Laboratory Johns Hopkins, Laurel, United States, (4)Observatorio Astronómico Nacional, Universidad Nacional de Colombia, Bogotá, Colombia
Machine Learning Aided Predictions of Magnetic Flux and Continuum Intensity During the Emergence of Active Regions (1960949)
Jonas Tirona1, Sarang Patil2, Eren Dogan3, Spiridon Kasapis4, Irina Kitiashvili5, Mengjia Xu2 and Alexander G Kosovichev6, (1)New Jersey Institute of Technology, Newark, United States, (2)New Jersey Institute of Technology, Data Science, Newark, United States, (3)New Jersey Institute of Technology, Edison, NJ, United States, (4)Princeton University, Department of Astrophysical Sciences, Princeton, United States, (5)NASA Ames Research Center, Moffett Field, CA, United States, (6)New Jersey Institute of Technology, Physics, Newark, United States
Visualization Guidance In Solar Flare Prediction: Multi-Temporal AnalysisApproach For Machine Learning Hyperparameter Selection (1970171)
Prajun Trital, University of Alabama in Huntsville, Department of Computer Science, Huntsville, AL, United States, Timothy S. Newman, University of Alabama in Huntsville, Department of Computer Science, Huntsville, United States and Nikolai V Pogorelov, University of Alabama in Huntsville, Department of Space Science, Huntsville, AL, United States
Uncertainty Quantification for Solar Flare Intensity Forecasting (1981255)
Jinsu Hong, Georgia State University, Atlanta, GA, United States, Chetraj Pandey, Georgia State University, Computer Science, Atlanta, GA, United States and Berkay Aydin, Georgia State University, Computer Science, Atlanta, United States
A Self-Trained Deep-Learning Methodology for Automated Solar Filament Detection and Dataset Generation (1984386)
Ryan Goldberg, CUNY City College of New York, Department of Electrical Engineering, New York, United States, Donald Schmit, CU/CIRES-NOAA/NCEI, Boulder, United States, Fred Moshary, NOAA EPP/MSI Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies (CESSRST-II), New York, United States and Yingli Tian, Department of Electrical Engineering, The City College of the City University of New York, and CUNY-CREST, Department of Electrical Engineering, New York, United States
Improving CME Arrival Time Predictions Using MHD Ensemble Modeling, Machine Learning, and HI Data (1986898)
Syed Raza, University of Alabama in Huntsville, Huntsville, AL, United States, Talwinder Singh, Georgia State University, Atlanta, GA, United States and Nikolai V Pogorelov, University of Alabama in Huntsville, Department of Space Science, Huntsville, AL, United States
