Tolerancing the fabrication errors of static optical elements for ELT-size wide-field AO systems
Bibliographic record
Abstract
Advanced wide-field AO systems, such as Multi-Conjugate AO (MCAO) systems often require many static optical elements (mirror and lenses) in addition to the active ones (deformable mirrors). These static elements induce additional wave-front errors due to random fabrication errors such as polishing errors. For ELT-size AO systems, these optical elements can be very large, and thus their cost and availability critically depends on how of much fabrication error can be tolerated. Therefore, a rigorous tolerance analysis is absolutely critical. Requirements can, in principle be relaxed, on account that fabrication errors with spatial scales larger than the inter-actuator spacing of the deformable mirrors (DMs) can be corrected. However, this process is significantly complicated by the fact that these optical elements are often conjugated far away from the DMs, and therefore DM correction cannot be achieved over a wide field of view (FOV). In this paper, we present our tolerance analysis in the context of NFIRAOS, the first-light MCAO system for the Thirty Meter Telescope. We start from two top-level error budgets: the “on-axis” error budget, which specifies the acceptable residual wave-front error in the narrow 17”x17” science FOV; and the “off-axis” error budget, which specifies the acceptable residual wave-front error at the edge of the 2’ diameter technical FOV. The former directly relates to science image quality, whereas the latter directly relates to sky coverage. For different assumptions on the spatial power spectrum of the polishing errors, we derive the requirements on each optical element in NFIRAOS using a Monte-Carlo analysis of the predicted off-axis performance of the system with on axis AO correction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".