Evaluating Climate Variability of the Canonical Hot-Jupiters HD 189733b and HD 209458b through Multi-epoch Eclipse Observations
Bibliographic record
Abstract
Abstract Here we present the analysis of multi-epoch secondary eclipse observations of HD 189733b and HD 209458b as a probe of temporal variability in the planetary climate using both Spitzer channels 1 and 2 (3.6 and 4.5 μm). We expect hot-Jupiter atmospheres to be dynamic environments exhibiting time varying weather. However, it is uncertain to what extent temporal variability will be observable when considering disk integrated observations. We do not detect statistically significant variability and are able to place useful upper limits on the infrared variability amplitudes in these atmospheres. There are very few planets with multi-epoch observations at the required precision to probe variability in dayside emission. The observations considered in this study span several years, providing insight into temporal variability at multiple timescales. In the case of HD 189733b, the best-fit eclipse depths for the channel 2 observations exhibit a scatter of 102 ppm about a median depth of 1827 ppm and in channel 1 exhibit a scatter of 88 ppm about a median depth of 1481 ppm. For HD 209458b, the best-fit eclipse depths for the channel 2 observations exhibit a scatter of 22 ppm about a median depth of 1406 ppm, and in channel 1 exhibit a scatter of 131 ppm about a median depth of 1092 ppm. The precision and scatter in these observations allow us to constrain variability to less than (5.6% and 6.0%) and (12% and 1.6%) for channels (1, 2) of HD 189733b and HD 209458b, respectively.
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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 | 0.000 |
| 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 | 0.000 | 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 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".