A New Analysis of Eight Spitzer Phase Curves and Hot Jupiter Population Trends: Qatar-1b, Qatar-2b, WASP-52b, WASP-34b, and WASP-140b
Bibliographic record
Abstract
Abstract With over 30 phase curves observed during the warm Spitzer mission, the complete data set provides a wealth of information relating to trends and three-dimensional properties of hot Jupiter atmospheres. In this work we present a comparative study of seven new Spitzer phase curves for four planets with equilibrium temperatures T eq ∼ 1300K: Qatar-2b, WASP-52b, WASP-34b, and WASP-140b, as well as a reanalysis of the 4.5 μm Qatar-1b phase curve due to the similar equilibrium temperature. In total, five 4.5 μm phase curves and three 3.6 μm phase curves are analyzed here with a uniform approach. Using these new results, in combination with literature values for the entire population of published Spitzer phase curves of hot Jupiters, we present evidence for a linear trend of increasing hotspot offset with increasing orbital period, as well as observational evidence for two classes of planets in apparent redistribution versus equilibrium temperature parameter space, and tentative evidence for a dependence of hotspot offset on planetary surface gravity in our ∼1300 K sample. We do not find trends in apparent heat redistribution with orbital period or gravity. Nonuniformity in literature Spitzer data analysis techniques precludes a definitive determination of the sources or lack of trends.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 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".