Adaptive optics and site requirements for the search of Earth-like planets with ELTs
Bibliographic record
Abstract
Since 1995, expolanets discoveries have triggered a renewal of the permanent question about the possible presence of life outside the solar system. Direct detection and characterization of earth-like extrasolar planets orbiting main-sequence stars are now among the most exciting and challenging astronomical topics where new major scientific results from space missions and also from ground-based ELT are expected. To scale the performances of an ELT for exoplanets searching, we examine the relative impact of three fundamental parameters (the actuator pitch, the telescope diameter and the site) on the image contrast. Then, we calculate the planet/star flux ratio needed to reach SNR=3 in 10h (only the photon noise is considered) from long-exposure AO-PSF computed with PAOLA (a fast analytical code developed by one of us: L.J.) for different ELT sizes and AO parameters under different observational conditions (atmospheric turbulenece, star magnitude) with or without coronagraphy. We find that an actuator pitch of 0.1 m is optimal for exoplanet searching in the visible and near-IR from 10 to 40 pc. Lastly, we emphasize that the site choice is as important as the telescope size choice is: a 15m telescope is just enough for coronagraphic search for exo-earths at 10pc (SNR=3 in 10h) from the promising Dome C (Antarctica), while a 30m ELT is needed to succeed the same observation in the same time from the Mauna Kea.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".