Clouds and Clarity: Revisiting Atmospheric Feature Trends in Neptune-size Exoplanets
Bibliographic record
Abstract
Abstract Over the last decade, precise exoplanet transmission spectroscopy has revealed the atmospheres of dozens of exoplanets, driven largely by observatories like the Hubble Space Telescope. One major discovery has been the ubiquity of atmospheric aerosols, often blocking access to exoplanet chemical inventories. Tentative trends have been identified, showing that the clarity of planetary atmospheres may depend on equilibrium temperature. Previous work has often grouped dissimilar planets together in order to increase the statistical power of any trends, but it remains unclear from observed transmission spectra whether these planets exhibit the same atmospheric physics and chemistry. We present a reanalysis of a smaller, more physically similar sample of 15 exo-Neptune transmission spectra across a wide range of temperatures (200–1000 K). Using condensation cloud and hydrocarbon haze models, we find that the exo-Neptune population is best described by low cloud sedimentation efficiency ( f sed ∼ 0.1) and high metallicity (100 × solar). There is an intrinsic scatter of ∼0.5 scale height, perhaps evidence of stochasticity in these planets’ formation processes. Observers should expect significant attenuation in transmission spectra of Neptune-size exoplanets, up to 6 scale heights for equilibrium temperatures between 500 and 800 K. With JWST's greater wavelength sensitivity, colder (<500 K) planets should be high-priority targets given their clearer atmospheres, and the need to distinguish between the “super-puffs” and more typical gas-dominated 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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".