Adaptive optics designs for an infrared multi-object spectrograph on TMT
Why this work is in the frame
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Bibliographic record
Abstract
The Thirty Meter Telescope (TMT), the next generation giant segmented mirror telescope, will have unprecedented astronomical science capability. Since science productivity is greatly enhanced through the use of adaptive optics, the TMT science team has decided that adaptive optics should be implanted on all the IR instruments. We present the results of a feasibility study for the adaptive optics systems on the infrared multi-object spectrograph, IRMOS and report on the design concepts and architectural options. The IRMOS instrument is intended to produce integral field spectra of up to 20 objects distributed over a 5 arcminute field of regard. The IRMOS adaptive optics design is unique in that it will use multiple laser guidestars to reconstruct the atmospheric volume tomographically, then apply AO correction for each science direction independently. Such a scheme is made technically feasible and cost effective through the use of micro-electromechanical system (MEMS) deformable mirrors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it