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Review of Machine Learning Models for Solar Energetic Particle Prediction
Authors:
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
, et al. (51 additional authors not shown)
Abstract:
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 physical processes extending from the solar surface and corona through the heliosphere, offering insight i…
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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 physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.
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Submitted 17 June, 2026;
originally announced June 2026.
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Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning
Authors:
Spiridon Kasapis,
Irina N. Kitiashvili,
Paul Kosovich,
Alexander G. Kosovichev,
Viacheslav M. Sadykov,
Patrick O'Keefe,
Vincent Wang
Abstract:
Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly,…
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Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly, space exploration. In this work, we utilize the recently developed dataset that combines the Solar Dynamics Observatory/Helioseismic and Magnetic Imager's (SDO/HMI) Space weather HMI Active Region Patches (SHARP) and the Solar and Heliospheric Observatory/Michelson Doppler Imager's (SoHO/MDI) Space Weather MDI Active Region Patches (SMARP). We employ a suite of machine learning strategies, including Support Vector Machines (SVM) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 +- 0.1, which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive-negative imbalance) reveals a slight increase (+ 0.04 +- 0.05) in the accuracy of a 14-hour SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.
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Submitted 4 March, 2024;
originally announced March 2024.
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Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
Authors:
Atharv Yeoleka,
Sagar Patel,
Shreejaa Talla,
Krishna Rukmini Puthucode,
Azim Ahmadzadeh,
Viacheslav M. Sadykov,
Rafal A. Angryk
Abstract:
The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time series benchmark dataset recently created to serve the heliophysics community as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique features, with 24 quantitative features computed from the photospheric magnetic field maps of active regions, describing their precedent flare activity. In this study,…
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The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time series benchmark dataset recently created to serve the heliophysics community as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique features, with 24 quantitative features computed from the photospheric magnetic field maps of active regions, describing their precedent flare activity. In this study, for the first time, we systematically attacked the problem of quantifying the relevance of these features to the ambitious task of flare forecasting. We implemented an end-to-end pipeline for preprocessing, feature selection, and evaluation phases. We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters. We methodologically compared the results of different FSS algorithms, both on the multivariate time series and vectorized formats, and tested their correlation and reliability, to the extent possible, by using the selected features for flare forecasting on unseen data, in univariate and multivariate fashions. We concluded our investigation with a report of the best FSS methods in terms of their top-k features, and the analysis of the findings. We wish the reproducibility of our study and the availability of the data allow the future attempts be comparable with our findings and themselves.
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Submitted 29 September, 2021;
originally announced September 2021.
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Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
Authors:
Gelu Nita,
Manolis Georgoulis,
Irina Kitiashvili,
Viacheslav Sadykov,
Enrico Camporeale,
Alexander Kosovichev,
Haimin Wang,
Vincent Oria,
Jason Wang,
Rafal Angryk,
Berkay Aydin,
Azim Ahmadzadeh,
Xiaoli Bai,
Timothy Bastian,
Soukaina Filali Boubrahimi,
Bin Chen,
Alisdair Davey,
Sheldon Fereira,
Gregory Fleishman,
Dale Gary,
Andrew Gerrard,
Gregory Hellbourg,
Katherine Herbert,
Jack Ireland,
Egor Illarionov
, et al. (16 additional authors not shown)
Abstract:
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, model…
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The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.
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Submitted 22 June, 2020;
originally announced June 2020.