Photoelectron transport and associated Far Ultraviolet emissions: Model simulation and comparison with observations
Bibliographic record
Abstract
Photoelectrons are produced by solar Extreme Ultraviolet radiation and contribute significantly to the ionization and heat balances in planetary upper atmospheres. They are also the source of dayglow emissions, whose intensities may become comparable to weak or moderate dayside auroras. Proper modeling of photoelectrons and dayglow components is desirable for global auroral imaging, one of the core objectives of the SMILE mission. In many previous studies and model simulations, the transport effects of photoelectrons are neglected, so that the photoelectron distribution is controlled by a balance between local production and energy degradation. However, photoelectrons, when generated, can move along the magnetic field line. In particular, some of the photoelectrons may precipitate into the conjugate dark hemisphere and induce auroral-like emissions there, which was reported in realistic observations [Kil et al., 2020]. As a part of the SMILE Ultraviolet imager (UVI) model platform, we have recently developed an auroral/dayglow model that takes into account the interhemispheric transport of photoelectrons and/or secondary electrons, as well as their interaction with the ionosphere/thermosphere. In this study, we report the model simulation of the photoelectron generation and transport, and their induced UV emissions in both the dayside and nightside atmosphere. The simulation results are found to be in reasonable agreement with the realistic SSUSI/GUVI observations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".