OPERA, an automatic PSF reconstruction software for Shack-Hartmann AO systems: application to Altair
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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) <i>r</i><sub>0</sub> from the DM drive commands, which includes an estimation of the outer scale <i>L</i><sub>0</sub> 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.
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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 | 0.001 |
| 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