Solar and Space Plasma Dynamics

Neueste Paper

• PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning

  Kevin Lee, Alison J. March · 24. September 2026

  Space weather early warning depends on detecting solar wind transients in in-situ measurements at the first Sun-Earth Lagrange point (L1), before they reach Earth. Fixed thresholds can miss combined magnetic and plasma structure, and many learning methods provide a single anomaly score. We present t…

• PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images

  Daniel Collin, Yuri Shprits, Luca Chiarabini, Stefan J. Hofmeister, Nadja Klein, Guillermo Gallego · 23. September 2026

  Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably ass…

• Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of H{\alpha} 6562.8 A and Ca II 8542.1 A Spectra

  Ziyang Zhang, Qin Li, Vasyl B. Yurchyshyn, Kangwoo Yi, Haimin Wang, Wenda Cao, Bo Shen · 17. September 2026

  Strong chromospheric absorption lines such as H$\alpha$ 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere. Multilayer spectral inversion (MLSI) offers a physically interpretable framework for modeling these lines using a finite n…

• Discovering Dual-Origin Slow Wind from Solar Orbiter with Self-Supervised Contrastive Learning

  Henry Han, Jorge Yero Salazar · 25. August 2026

  Whether the slow solar wind originates from one coronal source or two distinct channels remains a central open question in heliophysics. Resolving this requires unsupervised separation of two populations that arrive at nearly the same bulk speed and differ mainly in heavy-ion composition. We present…

• A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares

  Sathvik Soman, Jason T. L. Wang, Haimin Wang, Haodi Jiang · 23. Juli 2026

  We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics Observatory (SDO). We consider 33 significant flar…

• Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning

  Yash Chaudhary, Jason T. L. Wang, Chunhui Xu, Yan Xu, Sen Zhang · 23. Juni 2026

  Solar flares, particularly those of the M- and X-class, have a significant impact on human life because of their potential to disrupt critical infrastructure and communication systems on Earth. Accurate prediction of solar flares is crucial for mitigating these risks, but the black-box nature of con…

• Review of Machine Learning Models for Solar Energetic Particle Prediction

  Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman, Ricky Egeland, Manolis Georgoulis, Angelos Vourlidas, Athanasios Papaioannou, Eleni Lavasa, Anastasios Anastasiadis, Giorgos Giannopoulos, Andres Munoz-Jaramillo, Bala Poduval, Irina N. Kitiashvili, Alexander G. Kosovichev, Viacheslav Sadykov, Soukaina Filali Boubrahimi, Tate T. Hutchins, Hameedullah A. Farooki, Manuel E. Cuesta, Leng Y. Khoo, Sungmin Pak, Robert Czarnota, Jamie S. Rankin, Jamey Szalay, Mitchell M. Shen, Georgios Livadiotis, Zigong Xu, David J. McComas, Nikolaos Sarlis, Dionissios Hristopulos, Arik Posner, Alec J. Engell, Mohammed AbuBakr Ali, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary, Christina O. Lee, Sigiava Aminalragia-Giamini, Constantinos Papadimitriou, Ingmar Sandberg, Savvas Raptis, Shah Muhammad Hamdi, Monica Laurenza, Mirko Stumpo, Sumanth A. Rotti, India Jackson, Aatiya Ali, Atilim Gunes Baydin, Nathan Schwadron, Subhamoy Chatterjee, Maher A. Dayeh, Gelu M. Nita, Patrick M. O'Keefe, Chun Jie Chong, Paul Kosovich, Russell D. Marroquin, Berkay Aydin, Petrus C. Martens, Lulu Zhao, Yang Chen, Yian Yu, Monica G. Bobra, Ward Manchester, Tamas Gombosi, Ming Zhang, Jesse Torres, Philip K. Chan, Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Kimberly Moreland, Hazel M. Bain, Samuel Hart, Michael J. Starkey, Alan G. Ling, Simone Benella · 19. Juni 2026

  Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physic…

• On the Energy Distribution of the Galactic Center Excess' Sources

  Florian List, Yujin Park, Nicholas L. Rodd, Eve Schoen, Florian Wolf · 16. Juni 2026

  The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter. Weighing against that conclusion are analyses showing evidence for dim point sources within the spatial structure of the emission. Due to technical limitations these analyses are purely spatial with all spectr…

• Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator

  Marco Marena, Qin Li, Haimin Wang, Haodi Jiang, Prajwal Shah, Bo Shen · 9. Juni 2026

  Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO. To extend EUV coronal context into earlier periods, we leverage the multi-decade availability of full-disk \HeI{} observations, whose absorption is modulated by coronal irradiance and magnetic topology …

• Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations

  Zhaoxin Yan, Jason T. L. Wang, Haimin Wang, Harim Lee, Ju Jing, Yan Xu, Chunhui Xu, Vasyl Yurchyshyn · 26. Mai 2026

  Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space environment and on Earth. In this study, we present a novel fusion model to forecast the geoeffectiveness of CME events. Our model combines convolution…

• Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning

  Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Yan Xu, Vasyl Yurchyshyn, Vincent Oria, Khalid A. Alobaid, Xiaoli Bai · 14. April 2026

  The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensi…

• Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

  Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang, Haimin Wang, Manolis K. Georgoulis, Wen He, Yasser Abduallah, Hameedullah A. Farooki, Yan Xu · 14. April 2026

  Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a …

• Uncertainty-Aware Solar Flare Regression

  Jinsu Hong, Chetraj Pandey, Berkay Aydin · 10. März 2026

  Current solar flare predictions often lack precise quantification of their reliability, resulting in frequent false alarms, particularly when dealing with datasets skewed towards extreme events. To improve the trustworthiness of space weather forecasting, it is crucial to establish confidence interv…

• ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

  H. T. R\"udisser, G. Nguyen, J. Le Lou\"edec, E. E. Davies, C. M\"ostl · 3. März 2026

  Interplanetary coronal mass ejections (ICMEs) are major drivers of space weather disturbances, posing risks to both technological infrastructure and human activities. Automatic detection of ICMEs in solar wind in situ data is essential for early warning systems. While several methods have been propo…

• Inspectorch: Efficient rare event exploration in solar observations

  C. J. Díaz Baso, I. J. Soler Poquet, C. Kuckein, M. van Noort, N. Poirier · 25. Februar 2026

  The Sun is observed in unprecedented detail, enabling studies of its activity on very small spatiotemporal scales. However, the large volume of data collected by our telescopes cannot be fully analyzed with conventional methods. Popular machine learning methods identify general trends from observati…

• Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions

  Subhamoy Chatterjee, Andres Munoz-Jaramillo, Anna Malanushenko · 23. Februar 2026

  Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domain…

• Investigating Nonlinear Quenching Effects on Polar Field Buildup in the Sun Using Physics-Informed Neural Networks

  Jithu J. Athalathil, Mohammed H. Talafha, Bhargav Vaidya · 19. Februar 2026

  The solar dynamo relies on the regeneration of the poloidal magnetic field through processes strongly modulated by nonlinear feedbacks such as tilt quenching (TQ) and latitude quenching (LQ). These mechanisms play a decisive role in regulating the buildup of the Sun's polar field and, in turn, the a…

• Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge

  Zixian Wu, Xuebao Li, Yanfang Zheng, Rui Wang, Shunhuang Zhang, Jinfang Wei, Yongshang Lv, Liang Dong, Zamri Zainal Abidin, Noraisyah Mohamed Shah, Hongwei Ye, Pengchao Yan, Xuefeng Li, Xiaojia Ji, Xusheng Huang, Xiaotian Wang, Honglei Jin · 19. Februar 2026

  In this work, we develop, for the first time, a supervised classification framework with class-dependent rewards (CDR) to predict $\geq$MM flares within 24 hr. We construct multiple datasets, covering knowledge-informed features and line-of sight (LOS) magnetograms. We also apply three deep learning…

• SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions

  Spiridon Kasapis, Eren Dogan, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan, Jake D. Butler, Jonas Tirona, Sarang Patil, Mengjia Xu · 21. Januar 2026

  The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the deve…

• Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction

  Pranjal Patil, Anli Ji, Berkay Aydin · 15. Januar 2026

  Accurate prediction of solar energetic particle events is vital for safeguarding satellites, astronauts, and space-based infrastructure. Modern space weather monitoring generates massive volumes of high-frequency, multivariate time series (MVTS) data from sources such as the Geostationary perational…

• Extreme Solar Flare Prediction Using Residual Networks with HMI Magnetograms and Intensitygrams

  Juyoung Yun, Jungmin Shin · 9. Januar 2026

  Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms. By detecting sunspots from intensitygrams and extracting magn…

• Spectral and Spatial Graph Learning for Multispectral Solar Image Compression

  Prasiddha Siwakoti, Atefeh Khoshkhahtinat, Piyush M. Mehta, Barbara J. Thompson, Michael S. F. Kirk, Daniel da Silva · 1. Januar 2026

  High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementar…

• A Physics Informed Neural Network For Deriving MHD State Vectors From Global Active Regions Observations

  Subhamoy Chatterjee, Mausumi Dikpati · 25. Dezember 2025

  Solar active regions (ARs) do not appear randomly but cluster along longitudinally warped toroidal bands ('toroids') that encode information about magnetic structures in the tachocline, where global-scale organization likely originates. Global MagnetoHydroDynamic Shallow-Water Tachocline (MHD-SWT) m…

• Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator

  Reza Mansouri, Dustin Kempton, Pete Riley, Rafal Angryk · 1. Dezember 2025

  The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential fo…

• Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records

  Shiyu Shen, Zhe Gao, Taifeng Chai, Yang Huang, Bin Pan · 1. Dezember 2025

  Deep learning has revolutionized solar image analysis, yet most approaches train task-specific encoders from scratch or rely on natural-image pretraining that ignores the unique characteristics of Solar Dynamics Observatory (SDO) data. We introduce SolarCHIP, a family of contrastively pretrained vis…