Wavelength Does Not Equal Pressure: Vertical Contribution Functions and Their Implications for Mapping Hot Jupiters
Bibliographic record
Abstract
Abstract Multi-band phase variations, in principle, allow us to infer the longitudinal temperature distributions of planets as a function of height in their atmospheres. For example, 3.6 μm emission originates from deeper layers of the atmosphere than 4.5 μm due to greater water vapor absorption at the longer wavelength. Because heat transport efficiency increases with pressure, we expect thermal phase curves at 3.6 μm to exhibit smaller amplitudes and greater phase offsets than at 4.5 μm—yet this trend is not observed. Of the seven hot Jupiters with full-orbit phase curves at 3.6 and 4.5 μm, all of them have greater phase amplitude at 3.6 μm than at 4.5 μm, while four of the seven exhibit a greater phase offset at 3.6 μm. We use a 3D radiative-hydrodynamic model to calculate theoretical phase curves of HD 189733b, assuming thermo-chemical equilibrium. The model exhibits temperature, pressure, and wavelength-dependent opacity, primarily driven by carbon chemistry: CO is energetically favored on the dayside, while CH4 is favored on the cooler nightside. Infrared opacity, therefore, changes by orders of magnitude between day and night, producing dramatic vertical shifts in the wavelength-specific photospheres, which would complicate eclipse or phase mapping with spectral data. The model predicts greater relative phase amplitude and greater phase offset at 3.6 μm than at 4.5 μm, in agreement with the data. Our model qualitatively explains the observed phase curves, but it is in tension with current thermo-chemical kinetics models that predict zonally uniform atmospheric composition due to the transport of CO from the hot regions of the atmosphere.
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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 | 0.002 |
| 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 | 0.000 | 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".