Bibliographic record
Abstract
Direct imaging is a powerful exoplanet discovery technique that is complementary to other techniques and offers great promise in the era of 30 m class telescopes. Space-based transit surveys have revolutionized our understanding of the frequency of planets at small orbital radii around Sun-like stars. The next generation of extremely large ground-based telescopes will have the angular resolution and sensitivity to directly image planets with R < 4 R⊕ around the very nearest stars. Here, we predict yields from a direct imaging survey of a volume-limited sample of Sun-like stars with the Mid-Infrared ELT Imager and Spectrograph (METIS) instrument, planned for the 39 m European Southern Observatory Extremely Large Telescope (ELT) that is expected to be operational towards the end of the decade. Using Kepler occurrence rates, a sample of stars with spectral types A-K within 6.5 pc, and simulated contrast curves based on an advanced model of what is achievable from coronagraphic imaging with adaptive optics, we estimated the expected yield from METIS using Monte Carlo simulations. We find the METIS expected yield of planets in the N2 band (10.10−12.40 μm) is 1.14 planets, which is greater than comparable observations in the L (3.70−3.95 μm) and M (4.70−4.90 μm) bands. We also determined a 24.6% chance of detecting at least one Jovian planet in the background limited regime assuming a 1 h integration. We calculated the yield per star and estimate optimal observing revisit times to increase the yield. We also analyzed a northern hemisphere version of this survey and found there are additional targets worth considering. In conclusion, we present an observing strategy aimed to maximize the possible yield for limited telescope time, resulting in 1.48 expected planets in the N2 band.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".