Integrated modeling of NFIRAOS: characterizing performance in the presence of vibration
Bibliographic record
Abstract
The Narrow Field InfraRed Adaptive Optics System (NFIRAOS) will be the first-light facility Multi-Conjugate Adaptive Optics (MCAO) system for the Thirty Meter Telescope (TMT). Historically, an important factor limiting the performance of an AO system has been the system vibrations and their impact on the delivered image quality. In order to leverage the extraordinary capabilities of the TMT, NFIRAOS must be capable of meeting the required optical performance and stability specifications while operating in the presence of the predicted vibration environments. Using the NRC-developed integrated modeling framework (NRCim), a detailed evaluation of the dynamic opto-mechanical performance of NFIRAOS was completed with several sources of structural disturbances (vibration from the telescope structure, from attached instruments, and internal to NFIRAOS). Multiple computational methodologies, including transient (time-series) and harmonic (transfer function) analyses combined with a Linear Optics Model (LOM), together with control system transfer functions for both common path and non-common-path disturbances were used to evaluate and compare the NFIRAOS delivered image quality. These procedures are described and the NFIRAOS performance in the presence of the various sources of vibration are reported. These sources include observatory vibration transmitted to NFIRAOS as well as internal sources such as the LGS WFS trombone, refrigeration, client instruments’ rotator and cable wrap.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".