The METIS AO system: bringing extreme adaptive optics to the mid-IR
Bibliographic record
Abstract
METIS (Mid-infrared E-ELT Imager and Spectrometer) is the mid-infrared instrument proposed for the European Extremely Large Telescope (E-ELT). METIS will be the first instrument in the mid-IR that will actually require an Adaptive Optics system in order to reach a performance close to the diffraction limit. Extending Adaptive Optics for the mid-IR from the current generation of telescopes to 30-42 meter telescopes is technically challenging, but appears at first sight significantly easier than at visible and near infrared wavelengths. Adaptive Optics has been demonstrated to deliver Strehl Ratios exceeding 95% on 6-8 meter class telescopes at 10 microns, but achieving this performance on E-ELTs under normal observation conditions, requires that several higher order effects are taken into account. The performance of a mid-IR AO system drops significantly if refractivity effects and atmospheric composition variations are not compensated. Reaching Strehl Ratios of over 90% in the L, M and N band will require special considerations and will impact the system design and control scheme of AO systems for mid-IR on ELTs. The METIS instrument has finalized its preliminary design phase and in this paper we present the results of our performance estimates of the METIS AO system. We have included the effects of refractivity and composition fluctuations on the performance of the AO system and we have investigated how these effects impact the science cases for mid-IR instrumentation on an ELT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".