Large Interferometer For Exoplanets (LIFE): IX. Assessing the Impact of Clouds on Atmospheric Retrievals at Mid-Infrared Wavelengths with a Venus-Twin Exoplanet
Bibliographic record
Abstract
The Large Interferometer For Exoplanets (LIFE) initiative aims to develop a space based mid-infrared (MIR) nulling interferometer to measure the thermal emission spectra of temperate terrestrial exoplanets. We investigate how well LIFE could characterize a cloudy Venus-twin exoplanet to: (1) test our retrieval routine on a realistic non-Earth-like MIR spectrum of a known planet, (2) investigate how clouds impact retrievals, (3) refine the LIFE requirements derived in previous Earth-centered studies. We run retrievals for simulated LIFE observations of a Venus-twin exoplanet orbiting a Sun-like star located 10 pc from the observer. By assuming different models (cloudy and cloud-free) we analyze the performance as a function of the quality of the LIFE observation. This allows us to determine how well atmosphere and clouds are characterizable depending on the quality of the spectrum. Our study shows that the current minimal resolution ($R=50$) and signal-to-noise ($S/N=10$ at $11.2μ$m) requirements for LIFE suffice to characterize the structure and composition of a Venus-like atmosphere above the cloud deck if an adequate model is chosen. However, we cannot infer cloud properties. The accuracy of the retrieved planet radius ($R_{pl}$), equilibrium temperature ($T_{eq}$), and Bond albedo ($A_B$) depend on the choice of model. Generally, a cloud-free model performs best and thus the presence of clouds cannot be inferred. This model dependence of retrieval results emphasizes the importance of developing a community-wide best-practice for atmospheric retrieval studies. If we consider higher quality spectra (especially $S/N=20$), we can infer the presence of clouds and pose first constraints on their structure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".