Longitudinally Resolved Spectral Retrieval (ReSpect) of WASP-43b
Bibliographic record
Abstract
Abstract Thermal phase variations of short-period planets indicate that they are not spherical cows: day-to-night temperature contrasts range from hundreds to thousands of degrees, rivaling their vertical temperature contrasts. Nonetheless, the emergent spectra of short-period planets have typically been fit using one-dimensional (1D) spectral retrieval codes that only account for vertical temperature gradients. The popularity of 1D spectral retrieval codes is easy to understand: they are robust and have a rich legacy in solar system atmospheric studies. Exoplanet researchers have recently introduced multidimensional retrieval schemes to interpret the spectra of short-period planets, but these codes are necessarily more complex and computationally expensive than their 1D counterparts. In this paper we present an alternative: phase-dependent spectral observations are inverted to produce longitudinally resolved spectra that can then be fit using standard 1D spectral retrieval codes. We test this scheme on the iconic phase-resolved spectra of WASP-43b and on simulated observations for the James Webb Space Telescope (JWST) using the open-source Pyrat Bay 1D spectral retrieval framework. Notably, we take the model complexity of the simulations one step further from previous studies by allowing for longitudinal variations in composition in addition to temperature. We show that performing 1D spectral retrieval on longitudinally resolved spectra is more accurate than applying 1D spectral retrieval codes to disk-integrated emission spectra, even though this is identical in terms of computational load. We find that for the extant Hubble and Spitzer observations of WASP-43b, the difference between the two approaches is negligible, but JWST phase measurements should be treated with longitudinally resolved spectral retrieval (ReSpect).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".