Impact of laser launch location on the performance of laser tomography and multiconjugate adaptive optics for extremely large telescopes
Bibliographic record
Abstract
Laser tomography and multiconjugate adaptive optics are under development for ground-based extremely large telescopes. Continuous wave sodium guide star lasers are planned for these systems, but their use raises some difficulties due to the extended nature of the beacons generated in the mesosphere and their spatiotemporal variability. We describe a performance analysis on the impact of laser launch telescope (LLT) location for the Thirty Meter Telescope (TMT) multiconjugate adaptive optics system. A semianalytical first-order noise propagation calculation is presented, supplemented by end-to-end Monte Carlo physical optics simulations. The principal conclusion of the study is that modestly superior performance is achieved with multiple LLT locations around the primary mirror, compared to a single central LLT behind the secondary mirror, but the largest value of any of these improvements is of the order of 20 nm rms for the expected wavefront sensor noise levels, suggesting that the final choice of geometry should depend primarily on the cost and complexity of implementation trade-off. This conclusion is also fully supported by the fact that, for the TMT 70 arcsec laser guide star (LGS) asterism, the fratricide effect reduces the performance of the central launch geometry by only a small amount. The reduction ranges from only a few nm rms at zenith to a few tens of nm at a 45° zenith angle in the worst case that the effect cannot be calibrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".