Multistar turbulence monitor: a new technique to measure optical turbulence profiles
Why this work is in the frame
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Bibliographic record
Abstract
The strength and vertical distribution of atmospheric turbulence is a key factor determining the performance of optical and infrared telescopes, with and without adaptive optics. Yet, this remains challenging to measure. We describe a new technique using a sequence of short-exposure images of a star field, obtained with a small telescope. Differential motion between all pairs of star images is used to compute the structure functions of longitudinal and transverse wavefront tilt for a range of angular separations. These are compared with theoretical predictions of simple turbulence models by means of a Markov Chain Monte Carlo optimization. The method is able to estimate the turbulence profile in the lower atmosphere, the total and free-atmosphere seeing, and the outer scale. We present results of Monte Carlo simulations used to verify the technique, and show some examples using data from the second AST3 telescope at Dome A in Antarctica.
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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 | 0.001 | 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