OPERA, an automatic PSF reconstruction software for Shack-Hartmann AO systems: application to Altair
Bibliographic record
Abstract
When doing high angular resolution imaging with adaptive optics (AO), it is of crucial importance to have an accurate knowledge of the point spread function associated with each observation. Applications are numerous: image contrast enhancement by deconvolution, improved photometry and astrometry, as well as real time AO performance evaluation. In this paper, we present our work on automatic PSF reconstruction based on control loop data, acquired simultaneously with the observation. This problem has already been solved for curvature AO systems. To adapt this method to another type of WFS, a specific analytical noise propagation model must be established. For the Shack-Hartmann WFS, we are able to derive a very accurate estimate of the noise on each slope measurement, based on the covariances of the WFS CCD pixel values in the corresponding sub-aperture. These covariances can be either derived off-line from telemetry data, or calculated by the AO computer during the acquisition. We present improved methods to determine 1) r0 from the DM drive commands, which includes an estimation of the outer scale L0 2) the contribution of the high spatial frequency component of the turbulent phase, which is not corrected by the AO system and is scaled by r0. This new method has been implemented in an IDL-based software called OPERA (Performance of Adaptive Optics). We have tested OPERA on Altair, the recently commissioned Gemini-North AO system, and present our preliminary results. We also summarize the AO data required to run OPERA on any other AO system.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".