Local Turbulence: Effects and causes
Bibliographic record
Abstract
Dome seeing is a known source of image quality degradation, but despite tremendous progress in wavefront control with the development of adaptive optics and environmental control through implementation of dome venting, surprisingly little is known about it quantitatively. We have found evidence of non-Kolmogorov dome turbulence from our observations with the imaka wide field adaptive optics system; PSFs seem to indicate an excess of high spatial frequencies and turbulence profiles reveal turbulence at negative conjugations. This has motivated the development of a new type of optical turbulence sensor called AIR-FLOW, Airborne Interferometric Recombiner: Fluctuations of Light at Optical Wavelengths. It is a non-redundant mask imaging interferometer that samples the optical turbulence passing through a measurement cell and it measures the two-dimensional optical Phase Structure Function. This is a useful tool to characterise different types of turbulence (e.g. Kolmogorov, diffusive turbulence, etc.). By fitting different models, we can determine parameters such as Cn 2 , r0, L0 or deviation from fully developed turbulence. The instrument was tested at the Canada France Hawaii Telescope, at the University of Hawaii 2.2-meter telescope (UH88'') and at the Observatoire de la C{\^o}te d'Azur. It is ruggedised and sensitive enough to detect changes with different dome vent configurations, as well as slow local variations of the index of refraction in the UH88'' telescope tube. The instrument is portable enough that it can be used to locate sources of turbulence inside and around domes, but it can also be used in an operational setting without affecting observations to characterise the local optical turbulence responsible for dome seeing. Thus, it could be used in real-time observatory control systems to configure vents and air handlers to effectively reduce dome seeing. We believe it could also be a tool for site surveys to evaluate dome seeing mitigation strategies in situ.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".