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Review of Machine Learning Models for Solar Energetic Particle Prediction

T0 review · 1 major / 0 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Review of machine learning models for solar energetic particle prediction compares datasets, architectures, inputs and outputs to extract good practices.

desk verdict This review maps ML work on SEP prediction but its value hinges on whether the source studies are comparable enough to support cross-model recommendations. read the letter →

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

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 · ORCID
classification astro-ph.SRcs.AI
keywords solarenergeticparticlesmachinelearningSEPpredictionspaceweatherneuralnetworkarchitecturestrainingdatasetsbestpracticesradiationhazards
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

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.

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.

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.

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Extended reading notes

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.

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.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

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 · score 0.0 of 10

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.

Assumptions & free parameters 0 free parameters · 0 assumptions · 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.

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0 comments
Cite this review

Pith. "Pith review of Review of Machine Learning Models for Solar Energetic Particle Prediction." pith.science (2026). https://pith.science/paper/6BCWPNYO

@misc{pith2026260619539,
  author       = {Pith},
  title        = {Pith review of: Review of Machine Learning Models for Solar Energetic Particle Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6BCWPNYO}},
  note         = {Machine review of arXiv:2606.19539}
}
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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SEP-PRISM Data: A multi-source dataset for solar energetic particle forecasting

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    A curated multi-source dataset with 14,464 daily samples (650 operational SEP positives) for 24-hour-ahead solar energetic particle forecasting, extending magnetic and CME predictors back to 1996 via regression alignment.

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Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.