Simultaneous sodium profile estimation and LGS SH-WFS pixel processing optimization using LGS sub-aperture images
Bibliographic record
Abstract
Image displacement pixel processing for laser guide star (LGS) Shack–Hartmann wavefront sensors (WFS) is often based upon a center of gravity with thresholding [thresholded CoG (tCoG)] algorithm. This method yields a nearly linear response to the sub-aperture wavefront gradient, but suffers from zero-point biases due to sodium profile variability and the resulting changes in the shape of the LGS sub-aperture images. This effect interacts with additional biases due to the image truncation caused by the limited field of view of the WFS sub-apertures, as well as from WFS non-common path aberration (NCPA). Natural guide star (NGS) truth wavefront sensors (TWFS) have been proposed to correct for the resulting aberrations in the reconstructed wave-front, and multiple such TWFS would be required to control anisoplanatism effects when there are multiple LGS. We describe a novel algorithm that estimates the sodium profile from time averaged sub-aperture images of one or multiple LGS using a system imaging model. This estimate can then be used to correct for the zero-point bias by adjusting the tCoG reference vector. This eliminates the need for an NGS TWFS to detect sodium profile induced aberrations, and a single TWFS with faint NGS would then be sufficient to monitor any variations in NCPA if needed, which greatly improves the sky coverage. The reconstructed sodium profile can also be used to build constrained matched filters, a noise-optimal alternative to tCoG that requires accurate knowledge of the sub-aperture LGS images and their spatial derivatives (and has yet to be demonstrated on sky). This new sodium profile reconstruction algorithm consequently eliminates the need for dithering LGS spots on sky to determine these derivatives, which greatly simplifies the implementation of matched filtering and also provides better performance. All of the necessary sodium profile estimation, bias computations, and matched filter optimizations can be done with a modern CPU (e.g., Intel Core i7-11700) at around a 0.1-Hz update rate as a background process for the real time controller. Our simulations of this new method are for center launch LGS, but we are confident the profile estimation algorithm will work equally well if not better for side launch LGS, even when there is increased image truncation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".