Cleaning Our Hazy Lens: Exploring Trends in Transmission Spectra of Warm Exoplanets
Bibliographic record
Abstract
Abstract Relatively little is understood about the atmospheric composition of temperate to warm exoplanets (equilibrium temperature T eq < 1000 K), as many of them are found to have uncharacteristically flat transmission spectra. Their flattened spectra are likely due to atmospheric opacity sources such as planet-wide photochemical hazes and condensation clouds. We compile the transmission spectra of 25 warm exoplanets previously observed by the Hubble Space Telescope and quantify the haziness of each exoplanet using a normalized amplitude of the water absorption feature (A H). By examining the relationships between A H and various planetary and stellar forcing parameters, we endeavor to find correlations of haziness associated with planetary properties. We adopt new statistical correlation tests that are more suitable for the small, nonnormally distributed warm exoplanet sample. Our analysis shows that none of the parameters have a statistically significant correlation with A H (p ≤ 0.01) with the addition of new exoplanet data, including the previously identified linear trends between A H and T eq or the hydrogen–helium envelope mass fraction (f HHe). This suggests that haziness in warm exoplanets is not simply controlled by any single planetary/stellar parameter. Among all the parameters we investigated, planet gravity (g p), atmospheric scale height (H), planet density (ρ p), orbital eccentricity (e), and age of the star (t age) have tentative correlations with A H. Specifically, lower H, higher g p, ρ p, e, or t age may lead to clearer atmospheres. We still need more observations and laboratory experiments to fully understand the complex physics and chemistry involved in creating hazy warm exoplanets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".