An empirical infrared transit spectrum of Earth: opacity windows and biosignatures
Bibliographic record
Abstract
Abstract The Atmospheric Chemistry Experiment Fourier Transform Spectrometer on the SCISAT satellite has been measuring infrared (IR) transmission spectra of Earth during Solar occultations since 2004. We use these data to build an IR transit spectrum of Earth. Regions of low atmospheric opacity, known as windows, are of particular interest, as they permit observations of the planet’s lower atmosphere. Even in the absence of clouds or refraction, imperfect transmittance leads to a minimum effective thickness of hmin ≈ 4 km in the 10–12 $\mu \mathrm{m}$ opacity window at a spectral resolution of R = 103. None the less, at R = 105, the maximum transmittance at the surface is around ${70}{{{\ \rm per\ cent}}}$. In principle, one can probe the troposphere of an Earth-like planet via high-dispersion transit spectroscopy in the mid-IR; in practice aerosols and/or refraction likely make this impossible. We simulate the transit spectrum of an Earth-like planet in the TRAPPIST-1 system. We find that a long-term near-IR (NIR) campaign with the James Webb Space Telescope(JWST) could readily detect CO2, establishing the presence of an atmosphere. A mid-IR campaign or longer NIR campaign would be more challenging, but in principle could detect H2O and the biosignatures O3 and CH4.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".