Atmospheric Retrieval of Terrestrial Solar System Planets for LIFE
Bibliographic record
Abstract
Context: A long-term goal of exoplanet research is to characterize the atmospheres of a sizable sample of temperate terrestrial exoplanets. Such studies will build our knowledge about the diversity of terrestrial worlds and enable the discovery of habitable or even inhabited worlds. To achieve this goal, missions capable of measuring the spectra of temperate terrestrial exoplanets have been proposed (LUVOIR/HabEx - optical & near-infrared; Large Interferometer For Exoplanets (LIFE)[1] - mid-infrared (MIR)). The MIR thermal emission measured by LIFE provides exclusive probes to important molecules (e.g. the potential bioindicators CH4, O3). Further, the MIR observations can provide constraints on a planet’s pressure-temperature (PT) profile, radius, and surface conditions. Methods & Results: We present results from our recent atmospheric retrieval studies. We investigated a cloud-free Earth- [2] and, to our knowledge for the first time, a cloudy Venus-twin [3] exoplanet around a sun-like star at 10 pc. We simulate the MIR planet emission spectra with petitRADTRANS (1D radiative transfer model) [4] and use LIFESim [5], to estimate the wavelength-dependent noise expected for exoplanet observations with LIFE. Our retrieval suite uses the atmospheric model petitRADTRANS and the MultiNest algorithm [6] for parameter estimation. We retrieve the planetary mass and radius, the PT profile, the surface pressure, the molecular abundances and the cloud parameters. By considering input spectra of different wavelength ranges, resolutions (R), and noise levels (S/N), we aim to determine the requirements to: discriminate Earth- from Venus-like MIR spectra, characterize the structure and composition of atmospheres, detect potential biomarkers in Earth-twin, infer the presence of clouds in atmospheres, constrain cloud structure and composition in a Venus-twin. We also discuss challenges in the analysis of MIR exoplanet spectra from LIFE via atmospheric retrievals and how differences in the quality of the spectra affect them. Conclusion: With these studies and an additional retrieval study for Earth at different times [7], we find first constraints for the instrument requirements for the LIFE interferometer and identify important limitations and challenges of MIR atmospheric retrieval studies for exoplanets. References: [1] Quanz, S. P., et al. 2022, A&A, 664:A21 [2] Konrad, B. S., et al. 2022, A&A, 664:A23 [3] Konrad, B.S., et al. 2023, arXiv e-prints, arXiv:2303.04727 [4] Mollière, P., et al., 2019, A&A, 627:A67 [5] Dannert, F. A., et al. 2022, A&A, 664:A22 [6] Feroz, F., et al., 2009, MNRAS, 398(4):1601–1614 [7] Alei, E., et al. 2022, A&A, 665:A106
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".