Tip/tilt point spread function reconstruction for laser guide star multi-conjugate adaptive optics
Bibliographic record
Abstract
In adaptive optics systems employing laser guide stars, the tip/tilt contribution to the long exposure point spread function must be estimated separately from the high-order tip/tilt removed point spread function because this component is estimated separately from a single or multiple low-order natural guide star wavefront sensors. This paper investigates this problem for laser guide star multi conjugate adaptive optics. The approach is based on the scheme developed by Flicker in 2003 [1], and consists in post-processing the measurement covariance matrix of multiple low-order natural guide star wavefront sensors controlling tip/tilt and tilt anisoplanatism. An innovative simulation model based "balanced" algorithm is introduced to capture error terms not accounted for in Flicker's algorithm. Sample enclosed energy results for the Thirty Meter Telescope multi conjugate adaptive optics system demonstrate the superiority of the balanced method and call for further analytical work and experimental validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".