Detecting Biosignatures in Nearby Rocky Exoplanets Using High-contrast Imaging and Medium-resolution Spectroscopy with the Extremely Large Telescope
Bibliographic record
Abstract
Abstract In the upcoming decades, one of the primary objectives in exoplanet science is to search for habitable planets and signs of extraterrestrial life in the Universe. Signs of life can be indicated by thermal-dynamical imbalance in terrestrial planet atmospheres. O2 and CH4 in the modern Earth’s atmosphere are such signs, commonly termed biosignatures. These biosignatures in exoplanetary atmospheres can potentially be detectable through high-contrast imaging instruments on future extremely large telescopes. To quantify the signal-to-noise ratio (S/N) with extremely large telescopes, we select up to 10 nearby rocky planets and simulate medium-resolution (R ∼ 1000) direct imaging of these planets using the Mid-infrared ELT Imager and Spectrograph (ELT/METIS, 3–5.6 μm) and the High Angular Resolution Monolithic Optical and Near-infrared Integral field spectrograph (ELT/HARMONI, 0.5–2.45 μm). We calculate the S/N for the detection of biosignatures including CH4, O2, H2O, and CO2. Our results show that GJ 887 b has the highest detection of S/N for biosignatures, and Proxima Cen b exhibits the only detectable CO2 among the targets for ELT/METIS direct imaging. We also investigate the TRAPPIST-1 system, the archetype of nearby transiting rocky planet systems, and compare the biosignature detection of transit spectroscopy with JWST versus direct spectroscopy with ELT/HARMONI. Our findings indicate JWST is more suitable for detecting and characterizing the atmospheres of transiting planet systems such as TRAPPIST-1 that are relatively further away and have smaller angular separations than more nearby nontransiting planets.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".