Statistics of turbulence parameters at Maunakea using the multiple wavefront sensor data of RAVEN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prior statistical knowledge of atmospheric turbulence is essential for designing, optimizing and evaluating tomographic adaptive optics systems. We present the statistics of the vertical profiles of C^2_N and the outer scale at Maunakea estimated using a SLOpe Detection And Ranging (SLODAR) method from on-sky telemetry taken by a multi-object adaptive optics (MOAO) demonstrator, called RAVEN, on the Subaru telescope. In our SLODAR method, the profiles are estimated by fitting the theoretical autocorrelations and cross-correlations of measurements from multiple Shack–Haltmann wavefront sensors to the observed correlations via the non-linear Levenberg–Marquardt Algorithm (LMA). The analytical derivatives of the spatial phase structure function with respect to its parameters for the LMA are also developed. From a total of 12 nights in the summer season, a large ground C^2_N fraction of 54.3 per cent is found, with median estimated seeing of 0.460 arcsec. This median seeing value is below the results for Maunakea from the literature (0.6–0.7 arcsec). The average C^2_N profile is in good agreement with results from the literature, except for the ground layer. The median value of the outer scale is 25.5 m and the outer scale is larger at higher altitudes; these trends of the outer scale are consistent with findings in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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