AIROPA III: testing simulated and on-sky data
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive optics (AO) images from the W. M. Keck Observatory have delivered numerous influential scientific results, including detection of multi-system asteroids, the supermassive black hole at the center of the Milky Way, and directly imaged exoplanets. Specifically, the precise and accurate astrometry these images yield was used to measure the mass of the supermassive black hole using orbits of the surrounding star cluster. Despite these successes, one of the major obstacles to improved astrometric measurements is the spatial and temporal variability of the point-spread function delivered by the instruments. Anisoplanatic and Instrumental Reconstruction of Off-axis PSFs for AO (AIROPA) is a software package for the astrometric and photometric analysis of AO images using point-spread function fitting together with the technique of point-spread function reconstruction. In AO point-spread function reconstruction, the knowledge of the instrument performance and of the atmospheric turbulence is used to predict the long-exposure point-spread function of an observation. We present the results of our tests using AIROPA on both simulated and on-sky images of the Galactic Center. We find that our method is very reliable in accounting for the static aberrations internal to the instrument, but it does not improve significantly the accuracy on sky, possibly due to uncalibrated telescope aberrations.
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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 | 0.000 |
| Open science | 0.000 | 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