The MOAO system of the IRMOS near-infrared multi-object spectrograph for TMT
Bibliographic record
Abstract
The near-Infrared Multi-Object Spectrograph (IRMOS) for TMT is one of the most powerful astronomical instruments ever envisioned. The combination of the collecting area of TMT, the unique image-sharpening capabilities of the Multi-Object Adaptive Optics (MOAO) system, and the multiplexing advantage of the multi-object integral-field spectra provided by the IRMOS back-end make it capable of addressing some of the leading scientific challenges of the coming decades. Here we present an overview of one potential IRMOS concept and then focus on the MOAO system. In particular we will describe our concept for the laser and natural guide star wavefront sensors, deformable mirrors and the calibration system of MOAO. For each of these design elements, we describe the key trade studies which help define each subsystem. From results of our studies, we assemble a MOAO ensquared energy budget. We find that 50% of the energy is ensquared within the 50 milli-arcsecond spatial pixel of the IRMOS integral field units for a wavelength of 1.65μm. Given the requirements placed on the MOAO system to achieve this performance, large ensquared energies can be achieved with even finer plate scales for wavelengths longer than 1.5μm.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".