The HIA MCAO laboratory bench
Bibliographic record
Abstract
This paper presents an update on the design and deployment of the HIA MCAO laboratory bench. This bench directly supports the development of NFIRAOS, the first light MCAO facility for the Thirty Meter Telescope. The bench implements a closed-loop MCAO system, with two magnetic DMs, four LGS Shack-Hartmann WFSs, two NGS T/T WFS, one NGS T/T/F WFS and one higher order Truth WFS, making up a scaled down version of NFIRAOS. The bench includes several artificial turbulence screens and reproduces realistic LGS spot elongations. It is driven by software in Matlab, frame-rates ranging from 1Hz to 15Hz. The goals of this bench are to anchor the NFIRAOS end-toend simulation tools; to exercise real-time LGS tomographic AO in a variety of well controlled conditions, such as faint and poorly corrected NGSs, non-uniformities in the sodium layer and field dependant Non-Common-Path Aberrations (NCPAs); develop and demonstrate calibration procedures, such as PSF reconstruction and tomographic reconstruction and correction of field dependant NCPAs; and to validate optimization methods that operate at 10+ second time scales, which is not tractable in a numerical simulation, such as matched filter update and Cn2 estimation using a SLODAR method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".