Large Interferometer For Exoplanets (LIFE)
Bibliographic record
Abstract
Context. Terrestrial exoplanets in the habitable zone are likely a common occurrence. The long-term goal is to characterize the atmospheres of dozens of such objects. 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 such exoplanets. Aims. We investigate how well LIFE could characterize a cloudy Venus-twin exoplanet. This allows us to: (1) test our atmospheric retrieval routine on a realistic non-Earth-like MIR emission spectrum of a known planet, (2) investigate how clouds impact retrievals, and (3) further refine the LIFE requirements derived in previous Earth-centered studies. Methods. We ran Bayesian atmospheric retrievals for simulated LIFE observations of a Venus-twin exoplanet orbiting a Sun-like star located 10 pc from the observer. The LIFESIM noise model accounted for all major astrophysical noise sources. We ran retrievals using different models (cloudy and cloud-free) and analyzed the performance as a function of the quality of the LIFE observation. This allowed us to determine how well the atmosphere and clouds are characterizable depending on the quality of the spectrum. Results. At the current minimal resolution (R = 50) and signal-to-noise (S /N = 10 at 11.2 μ m) requirements for LIFE, all tested models suggest a CO2-rich atmosphere (≥30% in mass fraction). Further, we successfully constrain the atmospheric pressure-temperature (P–T) structure above the cloud deck (P–T uncertainty ≤ ± 15 K). However, we struggle to infer the main cloud properties. Further, the retrieved planetary radius (Rpl), equilibrium temperature (Teq), and Bond albedo (AB) depend on the model. Generally, a cloud-free model performs best at the current minimal quality and accurately estimates Rpl, Teq, and AB. If we consider higher quality spectra (especially S/N = 20), we can infer the presence of clouds and pose first constraints on their structure. Conclusions. Our study shows that the minimal R and S/N 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. Crucially, the cloud-free model is preferred by the retrieval for low spectral qualities. We thus find no direct evidence for clouds at the minimal R and S/N requirements and cannot infer the thickness of the atmosphere. Clouds are only constrainable in MIR retrievals of spectra with S/N ≥ 20. The model dependence of our retrieval results emphasizes the importance of developing a community-wide best-practice for atmospheric retrieval studies.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".