Revisiting the Energy Budget of WASP-43b: Enhanced Day–Night Heat Transport
Bibliographic record
Abstract
Abstract The large day–night temperature contrast of WASP-43b has so far eluded explanation. We revisit the energy budget of this planet by considering the impact of reflected light on dayside measurements and the physicality of implied nightside temperatures. Previous analyses of the infrared eclipses of WASP-43b have assumed reflected light from the planet is negligible and can be ignored. We develop a phenomenological eclipse model including reflected light, thermal emission, and water absorption, and we use it to fit published Hubble and Spitzer eclipse data. We infer a near-infrared geometric albedo of 24% ± 1% and a cooler dayside temperature of 1483 ± 10 K. Additionally, we perform light curve inversion on the three published orbital phase curves of WASP-43b and find that each suggests unphysical, negative flux on the nightside. By requiring non-negative brightnesses at all longitudes, we correct the unphysical parts of the maps and obtain a much hotter nightside effective temperature of 1076 ± 11 K. The cooler dayside and hotter nightside suggest a heat recirculation efficiency of 51% for WASP-43b, essentially the same as for HD 209458b, another hot Jupiter with nearly the same temperature. Our analysis therefore reaffirms the trend that planets with lower irradiation temperatures have more efficient day–night heat transport. Moreover, we note that (1) reflected light may be significant for many near-IR eclipse measurements of hot Jupiters, and (2) phase curves should be fit with physically possible longitudinal brightness profiles—it is insufficient to only require that the disk-integrated light curve be non-negative.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".