Design tradeoffs for a high spectral resolution mid-infrared echelle spectrograph on the Thirty-Meter Telescope
Bibliographic record
Abstract
A feasibility design study was undertaken to assess the requirements of a mid-infrared echelle spectrograph (MIRES) with a resolving power of 120,000 and its associated mid-infrared adaptive optics (MIRAO) system on the Thirty-Meter Telescope. Our baseline design incorporates a 2K×2K Si:As array or array mosaic for the spectrograph and a 1K×1K Si:As array for the slit viewer. Various tradeoffs were studied to minimize risk and to optimize the sensitivity of the instrument. Major challenges are to integrate the spectrograph to the MIRAO system and, later, to an adaptive secondary, the procurement of a suitable window and large KRS-5 lenses, and the acquisition of large format mid-IR detector arrays suitable for the range of background conditions. We conclude that the overall risk is relatively low and there is no technical reason that should prevent this instrument from being ready for use at first light on the Thirty- Meter Telescope.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".