Increased sky coverage with optimal correction of tilt and tilt-anisoplanatism modes in laser-guide-star multiconjugate adaptive optics
Why this work is in the frame
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Bibliographic record
Abstract
Laser-guide-star multiconjugate adaptive optics (MCAO) systems require natural guide stars (NGS) to measure tilt and tilt-anisoplanatism modes. Making optimal use of the limited number of photons coming from such, generally dim, sources is mandatory to obtain reasonable sky coverage, i.e., the probability of finding asterisms amenable to NGS wavefront (WF) sensing for a predefined WF error budget. This paper presents a Strehl-optimal (minimum residual variance) spatiotemporal reconstructor merging principles of modal atmospheric tomography and optimal stochastic control theory. Simulations of NFIRAOS, the first light MCAO system for the thirty-meter telescope, using ~500 typical NGS asterisms, show that the minimum-variance (MV) controller delivers outstanding results, in particular for cases with relatively dim stars (down to magnitude 22 in the H-band), for which low-temporal frame rates (as low as 16 Hz) are required to integrate enough flux. Over all the cases tested ~21 nm rms median improvement in WF error can be achieved with the MV compared to the current baseline, a type-II controller based on a double integrator. This means that for a given level of tolerable residual WF error, the sky coverage is increased by roughly 10%, a quite significant figure. The improvement goes up to more than 20% when compared with a traditional single-integrator controller.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it