Solar Physics

Latest papers

17 papers2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Explainable Prediction of High-Speed Solar Wind Streams driven by Coronal Holes via Topological Data Analysis
Oct 26, 2026Francesco Marchetti, Sabrina Guastavino, Daniel CollinSolar Physics Spatiotemporal Forecasting
Dipartimento di Matematica “Tullio Levi-Civita”, University of Padova, Italy · Osservatorio Astrofisico di Torino, Istituto Nazionale di Astrofisica, Torino, Italy · Dipartimento di Matematica, University of Genova, Italy +2
Machine Learning Applications in the Solar Interior and Atmosphere
Oct 26, 2026Vishal Upendran, Carlos J. Díaz BasoSolar Physics Scientific ML
SETI Institute, Mountain View, CA, USA 94043 · Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA 94304 · Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, N-0315 Oslo, Norway +1
Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 Å to EUV Translation and Coronal Hole Segmentation
Oct 3, 2026Marco Marena, Andrés Muñoz Jaramillo, Qin Li +8Solar Physics Image Generation
New Jersey Institute of Technology, Newark, NJ · SouthWest Research Institute, Boulder, CO · Sam Houston State University, Huntsville, TX +1
MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams
Sep 30, 2026Ruoyu Wang, David FouheySolar Physics Image Reconstruction
NYU
PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Sep 23, 2026Kevin Lee, Alison J. MarchUnsupervised Anomaly Detection Interpretable Anomaly Detection
NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
Sep 22, 2026Sina Norouzi Kandalan, Haodi Jiang, Jason T. L. Wang +1Solar Physics Deep Ensembles
Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA · Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA · Department of Physics, New Jersey Institute of Technology, Newark, NJ 07102, USA
PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images
Sep 22, 2026Daniel Collin, Yuri Shprits, Luca Chiarabini +3Time Series Forecasting Distributional Regression
Space Physics and Space Weather, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany · Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany +5
Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra
Sep 16, 2026Ziyang Zhang, Qin Li, Vasyl B. Yurchyshyn +4Efficient Neural Network Inference Solar Physics
Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, USA · Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA · Big Bear Solar Observatory, New Jersey Institute of Technology, Big Bear City, CA, USA +1
Discovering Dual-Origin Slow Wind from Solar Orbiter with Self-Supervised Contrastive Learning
Aug 22, 2026Henry Han, Jorge Yero SalazarSolar Physics Deep Clustering
Department of Computer Science, Baylor University, One Bear Place, Waco, TX 76798, USA
A Modern ConvNet for Solar Filament Detection
Jul 27, 2026J. R. Hu, Q. Hao, Z. Zheng +3Solar Physics Convolutional Neural Networks
School of Astronomy and Space Science, Nanjing University, Nanjing 210023, China · Key Laboratory of Modern Astronomy and Astrophysics, Ministry of Education, Nanjing 210023, China · Institute of Science and Technology for Deep Space Exploration, Suzhou Campus, Nanjing University, Suzhou 215163, China
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
Jul 21, 2026Sathvik Soman, Jason T. L. Wang, Haimin Wang +1Solar Irradiance Forecasting Time Series Forecasting
New Jersey Institute of Technology, Newark, NJ, USA · Sam Houston State University, Huntsville, TX, USA
Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning
Jun 20, 2026Yash Chaudhary, Jason T. L. Wang, Chunhui Xu +2Solar Physics
New Jersey Institute of Technology Newark, NJ 07102, USA · State University of New York Oneonta, NY 13820, USA
Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator
Jun 7, 2026Marco Marena, Qin Li, Haimin Wang +3Image-to-Image Translation Solar Physics
Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology · Department of Physics, New Jersey Institute of Technology · Department of Computer Science, Sam Houston State University +2
Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
Apr 11, 2026Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang +6Space Weather Forecasting Time Series Forecasting
Institute for Space Weather Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA · Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA · Center for Solar-Terrestrial Research, New Jersey Institute of Technology, Newark, NJ 07102, USA +3
Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network
Apr 11, 2026Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang +6Solar Physics
Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA · Big Bear Solar Observatory, New Jersey Institute of Technology, 40386 North Shore Lane, Big Bear City, CA 92314, USA +4
Inspectorch: Efficient rare event exploration in solar observations
Feb 23, 2026C. J. Díaz Baso, I. J. Soler Poquet, C. Kuckein +2Solar Physics Anomaly Detection
Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, N-0315 Oslo, Norway · Rosseland Centre for Solar Physics, University of Oslo, P.O. Box 1029 Blindern, N-0315 Oslo, Norway · Instituto de Astrofísica de Canarias, C/Vía Láctea s/n, E-38205 La Laguna, Tenerife, Spain +3
Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge
Feb 18, 2026Zixian Wu, Xuebao Li, Yanfang Zheng +14Solar Physics Cost-Sensitive Learning
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, People′s Republic of China · State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, People′s Republic of China · School of software, Southeast University, Nanjing, People′s Republic of China +4

Oct 26, 2026J/K move · Enter open · S save

Explainable Prediction of High-Speed Solar Wind Streams driven by Coronal Holes via Topological Data Analysis

Francesco Marchetti, Sabrina Guastavino, Daniel Collin

Dipartimento di Matematica “Tullio Levi-Civita”, University of Padova, Italy · Osservatorio Astrofisico di Torino, Istituto Nazionale di Astrofisica, Torino, Italy · Dipartimento di Matematica, University of Genova, Italy+2

Solar Physics Spatiotemporal Forecasting Explainable Artificial Intelligence Topological Data Analysis

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High-speed solar wind streams (HSSs) originating from coronal holes (CHs) are an important driver of recurrent geomagnetic activity. Since HSSs are associated to coronal structures visible in extreme-ultraviolet observations, this provides a natural basis for forecasting several days in advance. In this work, we investigate whether the topology of coronal structures observed in extreme ultraviolet (EUV) images contains predictive information on the subsequent occurrence of CH-driven HSSs at Earth. To this end, we consider Topological Data Analysis (TDA), and in particular persistent homology, to classify EUV solar images according to the occurrence of HSSs at Earth four days later. The target is a binary classification of HSS occurrence at a four-day lead time, defined using an HSS interval catalog and solar wind speed thresholds. Several combinations of topological representations and classifiers are evaluated under a common cross-validation framework. The results indicate that the topology of EUV coronal structures alone contains predictive information on subsequent CH-driven HSS occurrence, despite the absence of explicit CH geometric descriptors in the model input. To investigate the origin of this predictive information, we develop a model-level explainability framework based on cross-validation-aggregated feature weights and spatial evidence maps. A fully traced example connects persistence diagrams and persistence-image features to the classifier output and to their spatial localization in the original EUV observation. The proposed framework provides a transparent and computationally lightweight approach for investigating the relationship between coronal topology and subsequent high-speed solar wind streams.