Ionospheric Electron Heating Associated With Pulsating Auroras: Joint Optical and PFISR Observations
Bibliographic record
Abstract
Abstract In a recent study, Liang et al. (2017, https://doi.org/10.1002/2017JA024127 ) repeatedly identified strong electron temperature ( T e ) enhancements when Swarm satellites traversed pulsating auroral patches. In this study, we use joint optical and Poker Flat Incoherent Scatter Radar (PFISR) observations to further investigate the F region plasma signatures related to pulsating auroras. On 19 March 2015 night, which contained multiple intervals of pulsating auroral activities, we identify a statistical trend, albeit not a one‐to‐one correspondence, of strong T e enhancements (~500–1000 K) in the upper F region ionosphere during the passages of pulsating auroras over PFISR. On the other hand, there is no discernible and repeatable density enhancement in the upper F region during pulsating auroral intervals. Collocated optical and NOAA satellite observations suggest that the pulsating auroras are composed of energetic electron precipitation with characteristic energy >10 keV, which is inefficient in electron heating in the upper F region. Based upon PFISR observations and simulations from Liang et al. (2017) model, we propose that thermal conduction from the topside ionosphere, which is heated by precipitating low‐energy electrons, offers the most likely explanation for the observed electron heating in the upper F region associated with pulsating auroras. Such a heating mechanism is similar to that underlying the “stable auroral red arcs” in the subauroral ionosphere. Our proposal conforms to the notion on the coexistence of an enhanced cold plasma population and the energetic electron precipitation, in magnetospheric flux tubes threading the pulsating auroral patch. In addition, we find a trend of enhanced ion upflows during pulsating auroral intervals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".