A New Proton‐Hydrogen‐Electron Transport Model for Simulating Optical Emissions From Proton Aurora and Comparison With Ground Observations
Bibliographic record
Abstract
Abstract Energetic proton precipitation from the magnetosphere plays an important role in the magnetosphere‐ionosphere‐thermosphere coupling and energy transfer. Proton precipitation causes hydrogen emissions, such as Hβ (486.1 nm), and also triggers the excitation of other emission lines such as the blue‐line (427.8 nm) and the green‐line (557.7 nm). In light of the growing availability of ground‐based proton auroral measurements in recent years, we revisit the proton auroral modeling in this study, with more focus on the application for interpreting ground observations. An accurate simulation of these optical emissions requires a comprehensive understanding of particle transport and collisions in the upper atmosphere, where the simultaneous consideration of precipitating protons, newly generated energetic hydrogen atoms, and secondary electrons is critical. For this purpose, we couple a 3D Monte‐Carlo proton transport model and an electron transport model. The integrated model framework can compute the emission rates of most major auroral emission lines/bands resulting from proton precipitation, along with self‐consistent calculation of the ionospheric electron density variations. The model results show improved agreement with ground optical observations in terms of the Hβ yield and the green‐to‐Hβ ratio compared to previous model studies. Our new model is a valuable tool for quantifying excitation and ionization due to proton aurora. It has the potential to leverage ground observations to infer precipitating conditions at high altitudes and even for studying magnetospheric activity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".