Current Closure and Joule Heating in Data‐Driven 3‐D Auroral Arc Simulations
Bibliographic record
Abstract
Abstract Discrete auroral arc systems, despite many symmetries, are three‐dimensional in nature, encapsulating latitude and longitude variations in precipitation and field‐aligned currents combined with important altitude variations in conductivities, hence closure currents. This study presents data‐driven, 3‐D numerical simulations of these processes based on a coordinated campaign of heterogeneous measurements collected from the Poker Flat Research Range during a sequence of Swarm spacecraft overpasses. These measurements include field‐aligned current, global‐scale convection flow, and auroral emissions, which are used to create top‐boundary drivers for auroral arc simulations. Six conjunctions between the spacecraft, all‐sky imagers, and radars are investigated and their measurements are used to simulate auroral arcs through multiple iterations per conjunction event. We look at different estimates of the background convection flow, assumptions about the energy distributions of electron precipitation, and along‐arc structures in field‐aligned current, and see what effect they have on current closure and Joule heating in auroral arc systems. Across the six conjunction events, 11 comparisons of auroral arc systems are presented, covering a catalog of 17 simulations in total. These comparisons allow us to look at the sensitivity of auroral arc systems to input parameters and envelop the simulations in a qualitative confidence interval. Our results suggest that discrete aurorae should be studied in three dimensions to fully understand field‐aligned current closure and, by extension, Magnetosphere‐Ionosphere‐Thermosphere coupling. Additionally, our results demonstrate that both large‐scale convection flows and specifics about the energy distributions of auroral precipitation can significantly affect current closure and Joule heating in auroral arc systems.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".