JWST Transit Spectra. I. Exploring Potential Biases and Opportunities in Retrievals of Tidally Locked Hot Jupiters with Clouds and Hazes
Bibliographic record
Abstract
Abstract The atmospheres on tidally locked planets likely exhibit large differences between their day- and night-sides. In this paper, we illustrate how the combined effects of aerosols and day–night temperature gradients shape transit spectra of tidally locked exoplanets and evaluate the implications for retrievals of atmospheric properties. We have developed a new code, Multi-dimensional Exoplanet TransIt Spectra (METIS), which can compute transit spectra for arbitrary longitude–latitude–altitude grids of temperature and pressure. Using METIS, we pair flexible treatments of clouds and hazes with simple parameterized day–night temperature gradients to compute transit spectra and perform retrieval experiments across a wide array of possible exoplanet atmospheric properties. Our key findings are that: (1) the presence of aerosols can increase the effects of day–night temperature gradients on transit spectra; (2) ignoring day–night temperature gradients when attempting to perform Bayesian parameter estimation will return biased results, even when aerosols are present; (3) when a day–night temperature gradient is accounted for in the retrieval, some spectra contain sufficient information to constrain temperatures and the width of the transition from day to night. The presence of clouds and hazes can actually tighten such constraints, but also weaken constraints on metallicity and reference pressure. These last findings are predicated on the assumptions made in parameterizing the day–night atmospheric structure and the assumption of thermochemical equilibrium. Our results imply that this may be a promising avenue to pursue and represent a step toward the larger goal of developing models and theory of adequate complexity to match the superior-quality data that will soon be available.
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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 | 0.001 | 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".