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Record W4414749801 · doi:10.5194/angeo-43-579-2025

Modulation of cosmic-ray ground-level enhancements by solar-wind stream interfaces: a case study

2025· article· en· W4414749801 on OpenAlexafffund
Olakunle Ogunjobi, W.T. Sivla

Bibliographic record

VenueAnnales Geophysicae · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsInterplanetary spaceflightAccelerationDiffusionParticle accelerationInterplanetary mediumInterplanetary magnetic fieldNeutron monitorScatteringModulation (music)

Abstract

fetched live from OpenAlex

Abstract. Ground-level enhancements (GLEs) provide crucial insights into the acceleration and transport of solar energetic particles (SEPs). We present a comprehensive analysis of GLE 72, which occurred on 10 September 2017, coinciding with a solar-wind stream interaction region (SIR) impacting Earth's magnetosphere. By combining multi-station neutron monitor observations with a focused transport model constrained by solar-wind data, we investigate how the SIR modulates the observed GLE pulse shape. Our analysis reveals that the turbulent magnetic field within the SIR significantly enhances pitch angle scattering rates, with the diffusion coefficient increasing by up to 200 % during the 6 h SIR crossing. This leads to a 60 % increase in the particle mean free path across the SIR. Our model successfully reproduces the observed gradual rise phase (>8 h) and prolonged decay, demonstrating that even moderate interplanetary disturbances can substantially alter SEP transport conditions. Our results challenge the traditional impulsive–gradual classification of GLEs, highlighting the need to consider interplanetary transport effects when interpreting these events. The findings of this study highlight the importance of integrating multi-point observations and advanced modelling to disentangle particle acceleration and transport processes in the complex medium of solar wind.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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