NEPTUNE’S DYNAMIC ATMOSPHERE FROM KEPLER K2 OBSERVATIONS: IMPLICATIONS FOR BROWN DWARF LIGHT CURVE ANALYSES
Bibliographic record
Abstract
ABSTRACT Observations of Neptune with the Kepler Space Telescope yield a 49 day light curve with 98% coverage at a 1 minute cadence. A significant signature in the light curve comes from discrete cloud features. We compare results extracted from the light curve data with contemporaneous disk-resolved imaging of Neptune from the Keck 10-m telescope at 1.65 microns and Hubble Space Telescope visible imaging acquired nine months later. This direct comparison validates the feature latitudes assigned to the K2 light curve periods based on Neptune's zonal wind profile, and confirms observed cloud feature variability. Although Neptune's clouds vary in location and intensity on short and long timescales, a single large discrete storm seen in Keck imaging dominates the K2 and Hubble light curves; smaller or fainter clouds likely contribute to short-term brightness variability. The K2 Neptune light curve, in conjunction with our imaging data, provides context for the interpretation of current and future brown dwarf and extrasolar planet variability measurements. In particular we suggest that the balance between large, relatively stable, atmospheric features and smaller, more transient, clouds controls the character of substellar atmospheric variability. Atmospheres dominated by a few large spots may show inherently greater light curve stability than those which exhibit a greater number of smaller features.
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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 | 0.002 |
| 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) | 0.001 | 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".