Longitudinally Resolved Phase-curve Retrievals of WASP-43b
Bibliographic record
Abstract
Thermal phase variations of exoplanets are a patent testimony of their multidimensional nature: day-to-night temperature contrasts range from hundreds to thousands of degrees. Nonetheless, the spectra of these planets have typically been fit using 1D retrieval codes that only account for vertical temperature gradients. Recent multi-dimensional retrieval schemes are generally based on linear combinations of 1D models, which are more liable to degeneracies and more computationally demanding. Here we present an alternative: phase-dependent spectral observations are inverted to produce longitudinally resolved spectra that can then be fitted using standard 1D spectral retrieval codes. We test this scheme on the phase-resolved spectra of WASP-43b and on simulated JWST observations using the open-source Pyrat Bay retrieval framework. We show that 1D spectral retrievals on longitudinally resolved spectra are more accurate than applying 1D spectral retrieval codes to disk-integrated emission spectra, highlighting the impact of longitudinal variations in composition in addition to temperature. In particular, we find that JWST phase measurements of WASP-43b should be treated with longitudinally resolved spectral retrieval.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".