pith. sign in

arxiv: 2606.19539 · v1 · pith:6BCWPNYOnew · submitted 2026-06-17 · 🌌 astro-ph.SR · cs.AI

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
show 68 more authors
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
This is my paper

Pith reviewed 2026-06-26 18:53 UTC · model grok-4.3

classification 🌌 astro-ph.SR cs.AI
keywords solar energetic particlesmachine learningSEP predictionspace weatherneural network architecturestraining datasetsbest practicesradiation hazards
0
0 comments X

The pith

Review of machine learning models for solar energetic particle prediction compares datasets, architectures, inputs and outputs to extract good practices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper reviews the machine learning models developed for predicting solar energetic particle events. It catalogs the datasets used to train those models and compares their architectures along with the inputs they receive and the outputs they generate. From these comparisons the authors synthesize insights and propose good practices for future work in the area. Readers should care because SEP events produce radiation hazards for aviation, spacecraft electronics and crewed missions beyond Earth orbit. Consolidating an emerging set of data-driven approaches alongside traditional physics-based methods can support more reliable forecasts that protect technology and exploration.

Core claim

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.

What carries the argument

Systematic comparison across published ML models of their training datasets, neural network architectures, input features from solar and heliospheric observations, and output predictions of SEP event properties.

Load-bearing premise

That the body of published ML studies for SEP prediction is sufficiently mature and comparable across papers to allow meaningful cross-model evaluation and extraction of reliable best practices.

What would settle it

A controlled experiment in which new SEP prediction models built with and without the review's recommended practices are evaluated on the same independent test set of events, showing no measurable difference in forecast skill, would falsify the utility of the extracted practices.

read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript reviews currently available machine learning models for solar energetic particle (SEP) prediction. It identifies the datasets used for training, compares their architectures, inputs, and outputs, and outlines good practices and recommendations for future research based on these insights.

Significance. If the underlying literature permits meaningful synthesis, the review could help standardize approaches in an emerging application of ML to space weather, potentially improving prediction reliability and guiding efficient use of observational datasets for radiation hazard mitigation.

major comments (1)
  1. Abstract (purpose statement): the claim that good practices and recommendations can be reliably extracted rests on the assumption that published ML SEP studies are sufficiently standardized in datasets, metrics, validation approaches, and prediction targets. The manuscript must explicitly assess and document heterogeneity across the reviewed papers (e.g., in a dedicated comparison table or subsection) to justify any synthesized recommendations; without this, the central descriptive and prescriptive claims cannot be verified as robust.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment highlighting the need to explicitly document heterogeneity to support synthesized recommendations. We address the point below.

read point-by-point responses

  1. Referee: Abstract (purpose statement): the claim that good practices and recommendations can be reliably extracted rests on the assumption that published ML SEP studies are sufficiently standardized in datasets, metrics, validation approaches, and prediction targets. The manuscript must explicitly assess and document heterogeneity across the reviewed papers (e.g., in a dedicated comparison table or subsection) to justify any synthesized recommendations; without this, the central descriptive and prescriptive claims cannot be verified as robust.

    Authors: We agree that an explicit assessment of heterogeneity strengthens the justification for recommendations. The manuscript already performs comparisons of architectures, datasets, inputs, and outputs across studies, which inherently surfaces variations in these elements. To make the heterogeneity fully transparent and directly support the prescriptive claims, we will add a dedicated subsection together with a summary comparison table that systematically tabulates differences in datasets, metrics, validation approaches, and prediction targets. This revision will allow readers to evaluate the robustness of the extracted good practices. revision: yes

Circularity Check

0 steps flagged

No circularity: review paper with no derivations or predictions

full rationale

This manuscript is a literature review whose stated purpose is to summarize existing ML models for SEP prediction, catalog datasets, compare architectures/inputs/outputs, and suggest practices. It contains no equations, no claimed first-principles derivations, no fitted parameters presented as predictions, and no self-citation chains used to justify uniqueness theorems or ansatzes. The reader's assessment of 0.0 is therefore correct; the paper's central claim rests on the external literature being reviewable rather than on any internal reduction of outputs to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review paper, the central claim rests on the completeness and accuracy of the literature survey rather than any new parameters, axioms, or entities introduced by the authors.

pith-pipeline@v0.9.1-grok · 6119 in / 991 out tokens · 28991 ms · 2026-06-26T18:53:11.563217+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

182 extracted references · 21 canonical work pages · 1 internal anchor

  1. [1]

    , keywords =

    Delay of Near-relativistic Electrons with Respect to Type III Radio Bursts throughout the Inner Heliosphere. , keywords =. doi:10.3847/1538-4357/adaa7c , adsurl =

    work page doi:10.3847/1538-4357/adaa7c

  2. [2]

    , keywords =

    Parker Solar Probe observations of He/H abundance variations in SEP events inside 0.5 au. , keywords =. doi:10.1051/0004-6361/202039299 , adsurl =

    work page doi:10.1051/0004-6361/202039299

  3. [3]

    , keywords =

    PSP/IS IS observations of the 29 November 2020 solar energetic particle event. , keywords =. doi:10.1051/0004-6361/202140967 , adsurl =

    work page doi:10.1051/0004-6361/202140967 2020

  4. [4]

    , year = 2019, month = dec, volume =

    Probing the energetic particle environment near the Sun. , year = 2019, month = dec, volume =. doi:10.1038/s41586-019-1811-1 , adsurl =

    work page doi:10.1038/s41586-019-1811-1 2019

  5. [5]

    1977 , month = jan, pages =

    International Cosmic Ray Conference , title =. 1977 , month = jan, pages =

    1977

  6. [6]

    , keywords =

    Particle acceleration by astrophysical shocks. , keywords =. doi:10.1086/182658 , adsurl =

    work page doi:10.1086/182658

  7. [7]

    , keywords =

    The acceleration of cosmic rays in shock fronts - I. , keywords =. doi:10.1093/mnras/182.2.147 , adsurl =

    work page doi:10.1093/mnras/182.2.147

  8. [8]

    International Cosmic Ray Conference , year = 1983, series =

    Time-Dependent Shock Acceleration: Approximations and Exact Solutions. International Cosmic Ray Conference , year = 1983, series =

    1983

  9. [9]

    , keywords =

    Parallel and Perpendicular Diffusion of Energetic Particles in the Near-Sun Solar Wind Observed by Parker Solar Probe. , keywords =. doi:10.3847/2041-8213/ae063f , adsurl =

    work page doi:10.3847/2041-8213/ae063f 2041

  10. [10]

    , keywords =

    Distinct Solar Energetic Particle Shock Intensity─Diffusion Coefficient Relationships in the Inner Heliosphere. , keywords =. doi:10.3847/2041-8213/ae109c , adsurl =

    work page doi:10.3847/2041-8213/ae109c 2041

  11. [11]

    H., Cohen, C

    Magnetic field line random walk and solar energetic particle path lengths. Stochastic theory and PSP/IS IS observations. , keywords =. doi:10.1051/0004-6361/202039816 , archivePrefix =. 2011.08329 , primaryClass =

    work page doi:10.1051/0004-6361/202039816 2011

  12. [12]

    , keywords =

    Solar Energetic Particle Acceleration at a Spherical Shock with the Shock Normal Angle \ \ \_\ \ B\ \_\ n\ \ Evolving in Space and Time. , keywords =. doi:10.3847/1538-4357/ac9f43 , archivePrefix =. 2211.05366 , primaryClass =

    work page doi:10.3847/1538-4357/ac9f43

  13. [13]

    Space Weather , volume=

    Interpretable machine learning to forecast SEP events for solar cycle 23 , author=. Space Weather , volume=. 2022 , publisher=

    2022

  14. [14]

    The Astrophysical Journal , volume=

    Forecasting Solar Energetic Particle Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning , author=. The Astrophysical Journal , volume=. 2024 , publisher=

    2024

  15. [15]

    , keywords =

    Predicting Solar Energetic Particles: Solar Storm Watch - Preparing for Space Odyssey. , keywords =. doi:10.1007/s11214-025-01211-4 , adsurl =

    work page doi:10.1007/s11214-025-01211-4

  16. [16]

    Space Weather , keywords =

    High Energy Solar Particle Events and Their Relationship to Associated Flare, CME and GLE Parameters. Space Weather , keywords =. doi:10.1029/2022SW003334 , archivePrefix =. 2303.03935 , primaryClass =

    work page doi:10.1029/2022sw003334

  17. [17]

    The Astrophysical Journal , volume=

    Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-weather Active Region Patches as Potential Tool for Solar Flare Forecasting , author=. The Astrophysical Journal , volume=. 2024 , publisher=

    2024

  18. [18]

    The Astrophysical Journal Supplement Series , volume=

    SMARPs and SHARPs: Two solar cycles of active region data , author=. The Astrophysical Journal Supplement Series , volume=. 2021 , publisher=

    2021

  19. [19]

    The Astrophysical Journal Supplement Series , volume=

    Predicting Solar Proton Events of Solar Cycles 22--24 Using GOES Proton and Soft-X-Ray Flux Features , author=. The Astrophysical Journal Supplement Series , volume=. 2024 , publisher=

    2024

  20. [20]

    The Astrophysical Journal Supplement Series , volume=

    Improving solar energetic particle event prediction through multivariate time series data augmentation , author=. The Astrophysical Journal Supplement Series , volume=. 2024 , publisher=

    2024

  21. [21]

    The Astrophysical Journal Supplement Series , volume=

    An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data , author=. The Astrophysical Journal Supplement Series , volume=. 2025 , publisher=

    2025

  22. [22]

    2017 IEEE international conference on big data (big data) , pages=

    On the prediction of> 100 MeV solar energetic particle events using GOES satellite data , author=. 2017 IEEE international conference on big data (big data) , pages=. 2017 , organization=

    2017

  23. [23]

    Space Weather , volume=

    Toward enhanced prediction of high-impact solar energetic particle events using multimodal time series data fusion models , author=. Space Weather , volume=. 2024 , publisher=

    2024

  24. [24]

    Journal of Space Weather and Space Climate , volume=

    Forecasting solar energetic proton integral fluxes with bi-directional long short-term memory neural networks , author=. Journal of Space Weather and Space Climate , volume=. 2023 , publisher=

    2023

  25. [25]

    Space Weather , volume=

    Predicting solar energetic proton events (E> 10 MeV) , author=. Space Weather , volume=. 2011 , publisher=

    2011

  26. [26]

    2018 , publisher=

    Solar particle radiation storms forecasting and analysis: The HESPERIA HORIZON 2020 project and beyond , author=. 2018 , publisher=

    2020

  27. [27]

    Space Weather , volume=

    A machine learning approach to predicting SEP events using properties of coronal mass ejections , author=. Space Weather , volume=. 2022 , publisher=

    2022

  28. [28]

    Space Weather , volume=

    A machine learning approach to predicting SEP proton intensity and events using time series of relativistic electron measurements , author=. Space Weather , volume=. 2025 , publisher=

    2025

  29. [29]

    Advancing Solar Energetic Particle Event Prediction through Survival Analysis and Cloud Computing. I. Kaplan--Meier Estimation and Cox Proportional Hazards Modeling , author=. The Astrophysical Journal Supplement Series , volume=. 2024 , publisher=

    2024

  30. [30]

    2024 , version =

    Jackson, India and Petrus Martens , publisher =. 2024 , version =. doi:10.7910/DVN/GXY9MZ , url =

    work page doi:10.7910/dvn/gxy9mz 2024

  31. [31]

    Journal of Space Weather and Space Climate , volume=

    Solar energetic particle event occurrence prediction using solar flare soft X-ray measurements and machine learning , author=. Journal of Space Weather and Space Climate , volume=. 2021 , publisher=

    2021

  32. [32]

    Solar Physics , volume=

    Assessing the predictability of solar energetic particles with the use of machine learning techniques , author=. Solar Physics , volume=. 2021 , publisher=

    2021

  33. [33]

    Reports on Progress in Physics , volume=

    An introduction to the theory of diffusive shock acceleration of energetic particles in tenuous plasmas , author=. Reports on Progress in Physics , volume=. 1983 , publisher=

    1983

  34. [34]

    Space Weather , volume=

    Prediction of solar energetic particle event peak proton intensity using a simple algorithm based on CME speed and direction and observations of associated solar phenomena , author=. Space Weather , volume=. 2018 , publisher=

    2018

  35. [35]

    The Astrophysical Journal , volume=

    Short-term Classification of Strong Solar Energetic Particle Events Using Multivariate Time-series Classifiers , author=. The Astrophysical Journal , volume=. 2024 , publisher=

    2024

  36. [36]

    The Astrophysical Journal , volume=

    Precise and Accurate Short-term Forecasting of Solar Energetic Particle Events with Multivariate Time-series Classifiers , author=. The Astrophysical Journal , volume=. 2024 , publisher=

    2024

  37. [37]

    The Astrophysical Journal Supplement Series , volume=

    Integrated geostationary solar energetic particle events catalog: GSEP , author=. The Astrophysical Journal Supplement Series , volume=. 2022 , publisher=

    2022

  38. [38]

    arXiv preprint arXiv:1909.07872 , year=

    sktime: A unified interface for machine learning with time series , author=. arXiv preprint arXiv:1909.07872 , year=

    arXiv 1909

  39. [39]

    Space Weather , volume=

    SPRINTS: A framework for solar-driven event forecasting and research , author=. Space Weather , volume=. 2017 , publisher=

    2017

  40. [40]

    Forecasting the probability of solar energetic particle event occurrence using a multivariate ensemble of convolutional neural networks , author=

    MEMPSEP-I. Forecasting the probability of solar energetic particle event occurrence using a multivariate ensemble of convolutional neural networks , author=. Space Weather , volume=. 2024 , publisher=

    2024

  41. [41]

    Forecasting the properties of solar energetic particle events using a multivariate ensemble approach , author=

    MEMPSEP-II. Forecasting the properties of solar energetic particle events using a multivariate ensemble approach , author=. Space Weather , volume=. 2024 , publisher=

    2024

  42. [42]

    A Machine Learning-Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach , author=

    MEMPSEP-III. A Machine Learning-Oriented Multivariate Data Set for Forecasting the Occurrence and Properties of Solar Energetic Particle Events Using a Multivariate Ensemble Approach , author=. Space Weather , volume=. 2024 , publisher=

    2024

  43. [43]

    arXiv preprint arXiv:2502.08555 , year=

    A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting , author=. arXiv preprint arXiv:2502.08555 , year=

    arXiv

  44. [44]

    Space Science Reviews , volume=

    Galactic cosmic rays throughout the heliosphere and in the very local interstellar medium , author=. Space Science Reviews , volume=. 2022 , publisher=

    2022

  45. [45]

    Space Science Reviews , volume=

    Theory of cosmic ray transport in the heliosphere , author=. Space Science Reviews , volume=. 2022 , publisher=

    2022

  46. [46]

    Living Reviews in Solar Physics , volume=

    Space weather: The solar perspective: An update to Schwenn (2006) , author=. Living Reviews in Solar Physics , volume=. 2021 , publisher=

    2006

  47. [47]

    The Astrophysical Journal Supplement Series , volume=

    Analysis of SEP events and their possible precursors based on the GSEP Catalog , author=. The Astrophysical Journal Supplement Series , volume=. 2023 , publisher=

    2023

  48. [48]

    2022 , version =

    Rotti, Sumanth and Aydin, Berkay and Georgoulis, Manolis and Martens, Petrus , publisher =. 2022 , version =. doi:10.7910/DVN/DZYLHK , url =

    work page doi:10.7910/dvn/dzylhk 2022

  49. [49]

    2020 IEEE International Conference on Data Mining (ICDM) , pages=

    Fast and accurate time series classification through supervised interval search , author=. 2020 IEEE International Conference on Data Mining (ICDM) , pages=. 2020 , organization=

    2020

  50. [50]

    Markus Löning and Franz Király and Tony Bagnall and Matthew Middlehurst and Sajaysurya Ganesh and George Oastler and Jason Lines and Martin Walter and ViktorKaz and Lukasz Mentel and et al. , year=. sktime/sktime: v0.13.4 , DOI=

  51. [51]

    Atmosphere , volume=

    The Sun and space weather , author=. Atmosphere , volume=. 2022 , publisher=

    2022

  52. [52]

    Journal of Space Weather and Space Climate , volume=

    Solar flares, coronal mass ejections and solar energetic particle event characteristics , author=. Journal of Space Weather and Space Climate , volume=. 2016 , publisher=

    2016

  53. [53]

    Frontiers in Astronomy and Space Sciences , volume=

    Space Weather: From solar origins to risks and hazards evolving in time , author=. Frontiers in Astronomy and Space Sciences , volume=. 2022 , publisher=

    2022

  54. [54]

    Advances in Space Research , year=

    Prediction of solar energetic events impacting space weather conditions , author=. Advances in Space Research , year=

  55. [55]

    Living Reviews in Solar Physics , volume=

    Large gradual solar energetic particle events , author=. Living Reviews in Solar Physics , volume=. 2016 , publisher=

    2016

  56. [56]

    2021 , publisher=

    Solar energetic particles: a modern primer on understanding sources, acceleration and propagation , author=. 2021 , publisher=

    2021

  57. [57]

    Space Science Reviews , volume=

    The two sources of solar energetic particles , author=. Space Science Reviews , volume=. 2013 , publisher=

    2013

  58. [58]

    The Astrophysical Journal , volume=

    Diffusive shock acceleration and reconnection acceleration processes , author=. The Astrophysical Journal , volume=. 2015 , publisher=

    2015

  59. [59]

    Space Weather , volume=

    Advances in atmospheric radiation measurements and modeling needed to improve air safety , author=. Space Weather , volume=. 2015 , publisher=

    2015

  60. [60]

    69, 70 and 71 , author=

    Computation of dose rate at flight altitudes during ground level enhancements no. 69, 70 and 71 , author=. Advances in Space Research , volume=. 2015 , publisher=

    2015

  61. [61]

    Journal of Space Weather and Space Climate , volume=

    Retrospective analysis of GLEs and estimates of radiation risks , author=. Journal of Space Weather and Space Climate , volume=. 2018 , publisher=

    2018

  62. [62]

    PloS one , volume=

    How safe is safe enough? Radiation risk for a human mission to Mars , author=. PloS one , volume=. 2013 , publisher=

    2013

  63. [63]

    Advances in Space Research , volume=

    Understanding space weather to shield society: A global road map for 2015--2025 commissioned by COSPAR and ILWS , author=. Advances in Space Research , volume=. 2015 , publisher=

    2015

  64. [64]

    science , volume=

    Measurements of energetic particle radiation in transit to Mars on the Mars Science Laboratory , author=. science , volume=. 2013 , publisher=

    2013

  65. [65]

    Heliyon , volume=

    Towards sustainable horizons: A comprehensive blueprint for Mars colonization , author=. Heliyon , volume=. 2024 , publisher=

    2024

  66. [66]

    2022 ieee aerospace conference (aero) , pages=

    Artemis: an overview of NASA's activities to return humans to the Moon , author=. 2022 ieee aerospace conference (aero) , pages=. 2022 , organization=

    2022

  67. [67]

    Advances in Space Research , volume=

    Review of solar energetic particle prediction models , author=. Advances in Space Research , volume=. 2023 , publisher=

    2023

  68. [68]

    Validation of Solar Energetic Particle Forecasting Models for Space Radiation Operations with

    Whitman, Kathryn and Egeland, Ricky and Allison, Clayton and Quinn, Philip and Stegeman, Luke , journal=. Validation of Solar Energetic Particle Forecasting Models for Space Radiation Operations with. 2026 , publisher=

    2026

  69. [69]

    Science , year = 1884, month = nov, volume =

    The Numerical Measure of the Success of Predictions. Science , year = 1884, month = nov, volume =. doi:10.1126/science.ns-4.93.453 , adsurl =

    work page doi:10.1126/science.ns-4.93.453

  70. [70]

    , keywords =

    Cross calibration of NOAA GOES solar proton detectors using corrected NASA IMP-8/GME data. , keywords =. doi:10.1002/2014GL060469 , adsurl =

    work page doi:10.1002/2014gl060469

  71. [71]

    Calibration of the GOES-13/15 high energy proton detectors based on the PAMELA solar energetic particle observations

    Calibration of the GOES 13/15 high-energy proton detectors based on the PAMELA solar energetic particle observations. Space Weather , keywords =. doi:10.1002/2017SW001672 , archivePrefix =. 1708.05641 , primaryClass =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1002/2017sw001672

  72. [72]

    Space Weather , volume=

    A surrogate model for studying solar energetic particle transport and the seed population , author=. Space Weather , volume=. 2023 , publisher=

    2023

  73. [73]

    Universe , volume=

    Predicting> 10 MeV SEP events from solar flare and radio burst data , author=. Universe , volume=. 2020 , publisher=

    2020

  74. [74]

    All-Clear

    Prediction of Solar Proton Events with Machine Learning: Comparison with Operational Forecasts and" All-Clear" Perspectives , author=. arXiv preprint arXiv:2107.03911 , year=

    arXiv

  75. [75]

    Solar Physics , volume=

    The Helioseismic and Magnetic Imager (HMI) vector magnetic field pipeline: SHARPs--space-weather HMI active region patches , author=. Solar Physics , volume=. 2014 , publisher=

    2014

  76. [76]

    Advances in Space Research , volume=

    The Random Hivemind: An ensemble deep learning application to the solar energetic particle prediction problem , author=. Advances in Space Research , volume=. 2024 , publisher=

    2024

  77. [77]

    The Astrophysical Journal , volume=

    Statistical properties of soft X-ray emission of solar flares , author=. The Astrophysical Journal , volume=. 2019 , publisher=

    2019

  78. [78]

    Space Weather , volume=

    A technique for short-term warning of solar energetic particle events based on flare location, flare size, and evidence of particle escape , author=. Space Weather , volume=. 2009 , publisher=

    2009

  79. [79]

    The Astrophysical Journal , volume=

    Solar activity from 2006 to 2014 and short-term forecasts of solar proton events using the ESPERTA model , author=. The Astrophysical Journal , volume=. 2017 , publisher=

    2006

  80. [80]

    The Astrophysical Journal , volume=

    A short-term ESPERTA-based forecast tool for moderate-to-extreme solar proton events , author=. The Astrophysical Journal , volume=. 2018 , publisher=

    2018

Showing first 80 references.