A model for energetic electron microsignatures by Jupiter's moons and application for JUICE and Europa Clipper missions
Bibliographic record
Abstract
The term “microsignatures” refers to localized decreases in trapped particle flux caused by particle absorption by moons or rings that orbit within a planetary magnetosphere. Microsignatures can survive for hours and thus propagate far from the parent moon and its magnetospheric interaction region. They thus evolve under the influence of the background magnetospheric environment, meaning that their profile and location can trace processes in the magnetosphere itself, such as particle transport. Microsignatures are most common in electrons from few keV and up to the ultra-relativistic range (>10 MeV). A large number of microsignature detections at Saturn has revolutionized our understanding of energetic particle transport processes in the planet’s radiation belts. However, to this date, we lack a comprehensive survey of satellite microsignatures at Jupiter, even though few single case studies demonstrate their detectability in both the Galileo and Juno datasets. To proceed with their detailed study at Jupiter, it is necessary to first develop a framework that allows us to predict their timing and verify under which conditions, geometries and energies such predictions are most successful. The complex magnetospheric configuration at Jupiter (compared to the spin-aligned magnetic field of Saturn) introduces several challenges. In this work, we present the first steps towards developing this framework and contrast our initial predictions against energetic particle observations by Juno. In particular, we identify over thirty microsignatures from Jupiter’s innermost moons (Metis, Adrastea, Amalthea and Thebe), which orbit in a region where the effects of magnetic field asymmetries are greatly enhanced. By testing the predicted against the observed timing of those events, we optimize our model and our predictions for the JUICE and Europa Clipper missions at the Galilean moons. The long and continuous residence of both missions in the inner magnetosphere, particularly close to the orbits of Europa, Ganymede and Callisto, would result in unique geometries and opportunities to probe Jupiter’s inner and middle magnetosphere with numerous microsignature 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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".