Measuring line-of-sight sodium density structure using laser guide stars
Bibliographic record
Abstract
ABSTRACT The performance of adaptive optics systems employing sodium laser guide stars can be improved by continuously monitoring the vertical density structure of mesospheric sodium along the line of sight. We demonstrate that sodium density profiles can be retrieved by amplitude modulation of continuous wave (CW) lasers. In an experiment conducted at the Large Zenith Telescope (LZT), ESO’s Wendelstein Raman-fibre laser was amplitude-modulated with a pseudo-random binary sequence and profiles were obtained by cross-correlation of the modulation pattern with the observed return signal from the laser guide star. For comparison, high-resolution profiles were obtained simultaneously using the lidar system of the LZT. The profiles obtained by the two techniques show noise contamination, but were found to agree to within the measurement error. As a further check, a comparison was also made between several lidar profiles and those obtained by simultaneous observations using a remote telescope to image the laser plume from the side. The modulated CW lidar technique could be implemented by diverting a small fraction of the returned laser light to a photon counting detector. Theoretical analysis and numerical simulations indicate that, for 50 per cent modulation strength, the sodium centroid altitude could be retrieved every 5 s from a single laser guide star, with an accuracy which would induce a corresponding wavefront error of 50 nm for the Extremely Large Telescope and less than 30 nm for the Thirty Meter Telescope and Giant Magellan Telescope. If multiple laser guide stars are employed, the required modulation amplitude will be smaller.
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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 | 0.000 | 0.001 |
| 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 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".