Prebiosignature Molecules Can Be Detected in Temperate Exoplanet Atmospheres with JWST
Bibliographic record
Abstract
Abstract The search for biosignatures on exoplanets connects the fields of biology and biochemistry to astronomical observation, with the hope that we might detect evidence of active biological processes on worlds outside the solar system. Here we focus on a complementary aspect of exoplanet characterization connecting astronomy to prebiotic chemistry: the search for molecules associated with the origin of life, prebiosignatures. Prebiosignature surveys in planetary atmospheres offer the potential to both constrain the ubiquity of life in the galaxy and provide important tests of current prebiotic syntheses outside of the laboratory setting. Here, we quantify the minimum abundance of identified prebiosignature molecules that would be required for detection by transmission spectroscopy using JWST. We consider prebiosignatures on five classes of terrestrial planets: an ocean planet, a volcanic planet, a post-impact planet, a super-Earth, and an early-Earth analog. Using a novel modeling and detection test pipeline, with simulated JWST noise, we find the detection thresholds of hydrogen cyanide ( HCN ), hydrogen sulfide (H2S), cyanoacetylene (HC3N), ammonia (NH3), methane (CH4), acetylene (C2H2), sulfur dioxide (SO2), nitric oxide (NO), formaldehyde (CH2O), and carbon monoxide (CO) in a variety of low-mean-molecular-weight (<5) atmospheres. We test the dependence of these detection thresholds on an M dwarf target star and the number of observed transits, finding that a modest number of transits (1–10) are required to detect prebiosignatures in numerous candidate planets, including TRAPPIST-1e with a high-mean-molecular-weight atmosphere. We find that the Near Infrared Spectrograph G395M/H instrument is best suited for detecting most prebiosignatures.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".