Artificial Neural Network Model of High-Latitude Ionospheric Electric Potential: Hemispheric and Equinoctial Asymmetries
Bibliographic record
Abstract
The high-latitude ionospheric electric field plays a key role in ionospheric plasma dynamics and energetics. Various ground- and satellite-based observations have been utilized to develop empirical models of the convection electric field. Empirical modeling typically relies on statistical regression techniques in which predefined (and inherently biased) functions are fitted to measurements. In many convection models, it remains common practice to combine data from the Northern and Southern Hemispheres or to disregard differences between the March and September equinoxes. Such approaches make it challenging to identify important input variables and limit their ability to account for equinoctial and hemispheric asymmetries. These asymmetries, which are not fully understood, require further analysis and improved representation in models. In this work, we use nearly ten years of electric field data from the Swarm satellites’ Thermal Ion Imagers (TIIs) together with artificial neural networks (ANNs) to develop a model of high-latitude ionospheric electric potential. The Swarm ‘TII-ANN’ electric potential model explicitly incorporates the day of the year, universal time, solar and geomagnetic activity, 3-D interplanetary magnetic field, and 3-D solar wind velocity. Importantly, it also accounts for equinoctial and hemispheric variations. We describe the new model, validate its performance by comparing corresponding ion drifts to independent measurements from the DMSP satellite, and study the hemispheric and equinoctial asymmetries of high-latitude electric potential. Our results show that the cross-polar cap potential is larger in the Southern Hemisphere than in the Northern Hemisphere during the March equinox, with equinoctial asymmetry being particularly prominent in the south.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".