Strategies to cope with sodium layer profile variations in laser guide star AO systems
Bibliographic record
Abstract
The vertical profile of the mesospheric sodium layer varies significantly on a time scale of one minute. These variations can impact the random and systematic measurement errors of laser guide star Shack-Hartmann wave front sensors, particularly on extremely large telescopes. Sensor performance can be improved by selecting pixel processing weights matched to the sodium layer profile, assuming that the shape of the profile can be measured or estimated in real time. In this paper we describe the magnitude of these effects for the Thirty Meter Telescope AO system NFIRAOS. We review several existing approaches for measuring or estimating the sodium layer profile in real time. We then describe a new method for estimating the profile directly from the laser guide star wave front pixel intensities themselves, jointly with the subaperture tip/tilt measurements. The algorithm used for this purpose is based upon the multi-frame iterative blind deconvolution algorithm from image post processing: Subaperture tip/tilts and the sodium profile are estimated successively, bootstrapping the estimate of each quantity from the previous estimate of the other. We present promising initial simulation results on the potential performance of the algorithm, and suggest areas for future work.
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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 | 0.000 | 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 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".