Near-Infrared Transmission Spectroscopy of HAT-P-18$\,$b with NIRISS: Disentangling Planetary and Stellar Features in the Era of JWST
Bibliographic record
Abstract
The JWST Early Release Observations (ERO) included a NIRISS/SOSS (0.6-2.8$\,μ$m) transit of the $\sim\,$850$\,$K Saturn-mass exoplanet HAT-P-18$\,$b. Initial analysis of these data reported detections of water, escaping helium, and haze. However, active K dwarfs like HAT-P-18 possess surface heterogeneities $-$ starspots and faculae $-$ that can complicate the interpretation of transmission spectra, and indeed, a spot-crossing event is present in HAT-P-18$\,$b's NIRISS/SOSS light curves. Here, we present an extensive reanalysis and interpretation of the JWST ERO transmission spectrum of HAT-P-18$\,$b, as well as HST/WFC3 and $\textit{Spitzer}$/IRAC transit observations. We detect H$_2$O (12.5$\,σ$), CO$_2$ (7.3$\,σ$), a cloud deck (7.4$\,σ$), and unocculted starspots (5.8$\,σ$), alongside hints of Na (2.7$\,σ$). We do not detect the previously reported CH$_4$ ($\log$ CH$_4$ $<$ -6 to 2$\,σ$). We obtain excellent agreement between three independent retrieval codes, which find a sub-solar H$_2$O abundance ($\log$ H$_2$O $\approx -4.4 \pm 0.3$). However, the inferred CO$_2$ abundance ($\log$ CO$_2$ $\approx -4.8 \pm 0.4$) is significantly super-solar and requires further investigation into its origin. We also introduce new stellar heterogeneity considerations by fitting for the active regions' surface gravities $-$ a proxy for the effects of magnetic pressure. Finally, we compare our JWST inferences to those from HST/WFC3 and $\textit{Spitzer}$/IRAC. Our results highlight the exceptional promise of simultaneous planetary atmosphere and stellar heterogeneity constraints in the era of JWST and demonstrate that JWST transmission spectra may warrant more complex treatments of the transit light source effect.
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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 | 0.000 | 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".