The likelihood of detecting young giant planets with high-contrast imaging and interferometry
Bibliographic record
Abstract
ABSTRACT Giant planets are expected to form at orbital radii that are relatively large compared to transit and radial velocity detections (>1 au). As a result, giant planet formation is best observed through direct imaging. By simulating the formation of giant (0.3–5MJ) planets by core accretion, we predict planet magnitude in the near-infrared (2–4 μm) and demonstrate that, once a planet reaches the runaway accretion phase, it is self-luminous and is bright enough to be detected in near-infrared wavelengths. Using planet distribution models consistent with existing radial velocity and imaging constraints, we simulate a large sample of systems with the same stellar and disc properties to determine how many planets can be detected. We find that current large (8–10 m) telescopes have at most a 0.2 per cent chance of detecting a core-accretion giant planet in the L’ band and 2 per cent in the K band for a typical solar-type star. Future instruments such as METIS and VIKiNG have higher sensitivity and are expected to detect exoplanets at a maximum rate of 2 and 8 per cent, respectively.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".