Auroral currents from EMM and InSight: A comparison of EMM-EMUS auroral observations and InSight-IFG magnetic field fluctuations
Bibliographic record
Abstract
The Emirates Mars Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission spacecraft, which observes ultraviolet emission between approximately 100 and 170 nm, has observed multiple instances of nightside aurora at Mars. Variations in the auroral brightness and morphology have been observed to change on timescales of tens of minutes. The brightest aurorae are typically seen following space weather events, i.e., coronal mass ejection and stream interaction region impacts. The InSight Fluxgate Magnetometer (IFG) on the Interior Explorations using Seismic Investigations, Geodesy and Heat Transport (InSight) lander measured the magnetic field at the surface of Mars. IFG has measured variations in the nightside surface magnetic field, presumably due to variations in ionospheric and magnetospheric currents. Periodic and aperiodic variations in the surface field have been observed, including with timescales of a few minutes to tens of minutes. The magnitude of the fluctuations is often larger following space weather events. We examine the connection between the presence of aurora as observed by EMUS and surface magnetic field fluctuations as measured by IFG. Coincident EMUS and IFG observations show enhanced surface magnetic field fluctuations during times when aurorae were present. Additionally, the timescale of fluctuations in the auroral brightness are similar to the timescale of surface magnetic field fluctuations for non-coincident observations. These results suggest that IFG measured the surface magnetic field effect of time varying ionospheric auroral currents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".