New Models of Jupiter's Magnetopause and Bow Shock Through the <i>Juno</i> Prime Mission: Probabilistic Location, Shape, and Internally‐Driven Variation
Bibliographic record
Abstract
Abstract The interaction between Jupiter's magnetosphere and the solar wind is not well‐constrained: while internal energetic plasma processes are thought to dominate plasma circulation, the solar wind nonetheless exerts significant control over the shape and scale of the whole structure. To better constrain this interaction, we derive new functional forms for Jupiter's magnetopause and bow shock using data from the Ulysses , Galileo , Cassini , and Juno missions and calibrated solar wind estimates from the Multi‐Model Ensemble System for the Heliosphere (MMESH). We design an empirical Bayesian model to estimate the locations of the boundaries using a Markov‐chain Monte Carlo (MCMC) algorithm, expanding our model to sample all times, not only boundary crossing events. The boundary surfaces which best describe the data are thus estimated without the need for a full, physics‐based magnetohydrodynamic (MHD) treatment of the Jovian magnetosphere and the additional assumptions required for such. The new magnetopause model exhibits significant polar flattening and dawn‐dusk asymmetry, and includes a narrowing of the magnetotail when compared to previous models. The new bow shock model is largely axisymmetric. Both boundary models describe surfaces which lie closer to Jupiter than previous models, which has important implications for the modern picture of Jupiter's dynamic magnetosphere and the expected science results of current and upcoming Jupiter‐bound spacecraft. Applying these models to Juno 's trajectory, we estimate that the spacecraft should be expected to spend of each orbit in the magnetosheath and of each orbit in the solar wind starting from Perijove 64 (PJ64, 21 July 2021).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".