Awesome SOSS: atmospheric characterization of WASP-96 b using the JWST early release observations
Bibliographic record
Abstract
ABSTRACT The newly operational JWST offers the potential to study the atmospheres of distant worlds with precision that has not been achieved before. One of the first exoplanets observed by JWST in the summer of 2022 was WASP-96 b, a hot Saturn orbiting a G8 star. As a part of the Early Release Observations programme, one transit of WASP-96 b was observed with NIRISS/SOSS to capture its transmission spectrum from 0.6 to 2.85 μm. In this work, we utilize four retrieval frameworks to report precise and robust measurements of WASP-96 b’s atmospheric composition. We constrain the logarithmic volume mixing ratios of multiple chemical species in its atmosphere, including: H2O = $-3.59 ^{+ 0.35 }_{- 0.35 }$, CO2 = $-4.38 ^{+ 0.47 }_{- 0.57 }$, and K = $-8.04 ^{+ 1.22 }_{- 1.71 }$, thus generally consistent with 1× solar (with the exception of CO2). Notably, our results offer a first abundance constraint on potassium in WASP-96 b’s atmosphere and important inferences on carbon-bearing species such as CO2 and CO. Our short wavelength NIRISS/SOSS data are best explained by the presence of an enhanced Rayleigh scattering slope, despite previous inferences of a clear atmosphere – although we find no evidence for a grey cloud deck. Finally, we explore the data resolution required to appropriately interpret observations using NIRISS/SOSS. We find that our inferences are robust against different binning schemes. That is, from low R = 125 to the native resolution of the instrument, the bulk atmospheric properties of the planet are consistent. Our systematic analysis of these exquisite observations demonstrates the power of NIRISS/SOSS to detect and constrain multiple molecular and atomic species in the atmospheres of hot giant planets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".