Optimal Differential Astrometry for Multiconjugate Adaptive Optics. I. Astrometric Distortion Mapping using On-sky GeMS Observations of NGC 6723
Bibliographic record
Abstract
Abstract The Extremely Large Telescope and the Thirty Meter Telescope will use state of the art multiconjugate adaptive optics (MCAO) systems to obtain the full D 4 advantage that their apertures can provide. However, to reach the full astrometric potential of these facilities for on-sky science requires understanding any residual astrometric distortions from these systems and find ways to measure and eliminate them. In this work, we use Gemini multiconjugate adaptive optic system (GeMS) observations of the core of NGC 6723 to better understand the on-sky astrometric performance of MCAO. We develop new methods to measure the astrometric distortion fields of the observing system, which probe the distortion at the highest possible spatial resolution. We also describe methods for examining the time-variable and static components of the astrometric distortion. When applied to the GeMS Gemini South Adaptive Optics Imager (GSAOI) data, we are able to see the effect of the field rotator at the subpixel level, and we are able to empirically derive the distortion due to the optical design of GeMS/GSAOI. We argue that the resulting distortion maps are a valuable tool to measure and monitor the on-sky astrometric performance of future instrumentation. Our overall astrometry pipeline produces high-quality proper motions with an uncertainty floor of ∼45 μas yr−1. We measure the proper motion dispersion profile of NGC 6723 from a radius of ∼10″ out to ∼1′ based on ∼12,000 stars. We also produce a high-quality optical–near infrared color–magnitude diagram, which clearly shows the extreme horizontal branch and main-sequence knee of this cluster.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".