Microphysics of KCl and ZnS Clouds on GJ 1214 b
Bibliographic record
Abstract
Abstract Clouds in the atmospheres of exoplanets confound characterization efforts by reducing, eliminating, and distorting the spectral signatures of molecular abundances. As such, interpretations of exoplanet spectra strongly depend on the choice of cloud model, many of which are highly simplified and lack predictive power. In this work, we use a cloud model that incorporates microphysical processes to simulate potassium chloride (KCl) and zinc sulfide (ZnS) clouds in the atmosphere of the super-Earth GJ 1214 b and how they vary as a function of the strength of vertical mixing and the atmospheric metallicity. Microphysical processes control the size and spatial distribution of cloud particles, allowing for the computation of more physical cloud distributions than simpler models. We find that the mass and opacity of KCl clouds increase with mixing strength and metallicity, with the particle size and spatial distribution defined by nucleation, condensation, evaporation, and transport timescales. ZnS clouds cannot form without the presence of condensation nuclei, while heterogeneous nucleation of ZnS on KCl reduces particle sizes compared to pure KCl cases. In order to explain the flat transmission spectrum of GJ 1214 b with homogeneously nucleated KCl clouds, the atmospheric metallicity must be at least 1000× solar, and the eddy diffusivity must be at least 1010 cm2 s−1. We predict that James Webb Space Telescope observations of GJ 1214 b may reveal the presence of methane, carbon monoxide, and water, allowing for constraints to be placed on atmospheric metallicity and C/O ratio.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".