Optimal Stellar Photometry for Multi-conjugate Adaptive Optics Systems Using Science-based Metrics
Bibliographic record
Abstract
Abstract We present a detailed discussion of how to obtain precise stellar photometry in crowded fields using images from multi-conjugate adaptive optics (MCAO) systems, with the intent of informing the scientific development of this key technology for the Extremely Large Telescopes. We use deep J and K s exposures of NGC 1851 taken with the Gemini Multi-Conjugate Adaptive Optics System (GeMS) on Gemini South to quantify the performance of the instrument and to develop an optimal strategy for stellar photometry using point-spread function (PSF)-fitting techniques. We judge the success of the various methods we employ by using science-based metrics, particularly the width of the main sequence turnoff region. We also compare the GeMS photometry with the exquisite HST data in the visible of the same target. We show that the PSF produced by GeMS possesses significant spatial and temporal variability that must be accounted for during the analysis. We show that the majority of the variation of the PSF occurs within the “control radius” of the MCAO system and that the best photometry is obtained when the PSF radius is chosen to closely match this spatial scale. We identify photometric calibration as a critical issue for next-generation MCAO systems such as those on the Thirty Meter Telescope and European Extremely Large Telescope. Our final CMDs reach K s ∼ 22—below the main sequence knee—making it one of the deepest for a globular cluster available from the ground. Theoretical isochrones are in remarkable agreement with the stellar locus in our data from below the main sequence knee to the upper red giant branch.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".