Modulation of cosmic-ray ground-level enhancements by solar-wind stream interfaces: a case study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".