Science with CFHT's future high dynamic range AO system PUEO NUI
Bibliographic record
Abstract
Rethinking the efficient use of 4m-class telescopes in the dawning era of larger facilities is a timely but challenging debate. The extensive use of PUEO for imaging (and now spectroscopy) has kept CFHT at the forefront of scientific research with adaptive optics since its commissioning in 1996. Even though larger facilities are now starting to think about ways to implement high order AO systems, we believe the medium size of the CFHT and the excellent quality of the site on Mauna Kea is a perfect combination to reach the highest performances with a high order AO system. The fields of application of high order adaptive optics are exciting: They include extremely high contrast imaging and coronography in the near-infrared and diffraction-limited imaging in the optical, with the corresponding gain in angular resolution. In this paper we present a quick description of a few specific astrophysical problems that would benefit from an upgraded AO system at the Canada-France-Hawaii Telescope. More technical details about the upgrade of PUEO are presented by Lai et al. and Cuillandre et al. in these proceedings, see papers 4839-78 and 4839-31.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".