Hot-air optical turbulence generator for the testing of adaptive optics systems: principles and characterization
Bibliographic record
Abstract
A statistically repeatable, hot-air optical turbulence generator, based on the forced mixing of two air flows with different temperatures, is described. Characterization results show that it is possible to generate any turbulence strength up to CN2 Dh approximately 6 x 10(-10) m1/3, allowing a ratio of beam diameter to Fried's parameter as large as D/r0 approximately 25 for one crossing through the turbulator or D/r0 approximately 38 for two crossings. The outer scale (L0 approximately 133 +/- 60 mm) is found to be compatible with the turbulator mixing chamber size (170 mm), and the inner scale (l0 approximately 7.6 +/- 3.8 mm) is compatible with the values in the literature for the free atmosphere. The temporal power spectrum analysis of the centroid of the focused image shows good agreement with Kolmogorov's theory. Therefore the device can be used with confidence to emulate realistic turbulence in a controlled manner. A calibrated CN2 profile, both in layer altitude and strength, is necessary for the testing of off-axis adaptive optics correction (multiconjugate adaptive optics). Testing was done to calibrate the CN2 profile using the slope detection and ranging technique. The first results, with only one layer, show the validity of the approach and indicate that a multiple-pass scheme is viable with a few modifications of the current setup.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".