Direct Imaging in Reflected Light: Characterization of Older, Temperate Exoplanets With 30-m Telescopes
Bibliographic record
Abstract
Direct detection, also known as direct imaging, is a method for discovering and characterizing the atmospheres of planets at intermediate and wide separations. It is the only means of obtaining spectra of non-transiting exoplanets. Characterizing the atmospheres of planets in the <5 AU regime, where RV surveys have revealed an abundance of other worlds, requires a 30-m-class aperture in combination with an advanced adaptive optics system, coronagraph, and suite of spectrometers and imagers - this concept underlies planned instruments for both TMT (the Planetary Systems Imager, or PSI) and the GMT (GMagAO-X). These instruments could provide astrometry, photometry, and spectroscopy of an unprecedented sample of rocky planets, ice giants, and gas giants. For the first time habitable zone exoplanets will become accessible to direct imaging, and these instruments have the potential to detect and characterize the innermost regions of nearby M-dwarf planetary systems in reflected light. High-resolution spectroscopy will not only illuminate the physics and chemistry of exo-atmospheres, but may also probe rocky, temperate worlds for signs of life in the form of atmospheric biomarkers (combinations of water, oxygen and other molecular species). By completing the census of non-transiting worlds at a range of separations from their host stars, these instruments will provide the final pieces to the puzzle of planetary demographics. This whitepaper explores the science goals of direct imaging on 30-m telescopes and the technology development needed to achieve them.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".