Statistical Trends in JWST Transiting Exoplanet Atmospheres
Bibliographic record
Abstract
Abstract Our brains are hardwired for pattern recognition as correlations are useful for predicting and understanding nature. As more exoplanet atmospheres are being characterized with JWST, we are starting to unveil their properties on a population level. Here we present a framework for comparing exoplanet transmission spectroscopy from 3 to 5 μm with four bands: L (2.9–3.7 μm), SO2 (3.95–4.1 μm), CO2 (4.25–4.4 μm), and CO (4.5–4.9 μm). Together, the four bands cover the major carbon-, oxygen-, nitrogen-, and sulfur-bearing molecules including H2O, CH4, NH3, H2S, SO2, CO2, and CO. Among the eight high-precision gas giant exoplanet planet spectra we collect, we find strong correlations between the SO2 – L index and planet mass (r = −0.41 ± 0.09) and temperature (r = −0.64 ± 0.08), indicating SO2 preferably exists (SO2 – L > −0.5) among low-mass (∼ <0.3 M J) and cooler (∼ <1200 K) targets. We also observe strong temperature dependency for both CO2 – L and CO – L indices. Under equilibrium chemistry and isothermal thermal structure assumptions, we find that the planet sample favors supersolar metallicity and a low C/O ratio (<0.7). In addition, the presence of a mass–metallicity correlation is favored over uniform metallicity with the eight planets. We further introduce the SO2 – L versus CO2 – L diagram, similar to the color–magnitude diagram for stars and brown dwarfs. All reported trends here will be testable and will be further quantified with existing and future JWST observations within the next few years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".