The GeMS/GSAOI Galactic Globular Cluster Survey (G4CS). I. A Pilot Study of the Stellar Populations in NGC 2298 and NGC 3201
Bibliographic record
Abstract
Abstract We present the first results from the GeMS/GSAOI Galactic Globular Cluster Survey (G4CS) of the Milky Way globular clusters NGC 3201 and NGC 2298. Using the Gemini South Adaptive Optics Imager (GSAOI), in tandem with the Gemini Multi-conjugate adaptive optics System (GeMS) on the 8.1 m Gemini-South telescope, we collected deep near-IR observations of both clusters, resolving their constituent stellar populations down to K s ≃ 21 Vega mag. Point-spread function (PSF) photometry was performed on the data using spatially variable PSFs to generate JHK s photometric catalogs for both clusters. These catalogs were combined with Hubble Space Telescope (HST) data to augment the photometric wavelength coverage, yielding catalogs that span the near-UV to near-IR. We then applied 0.14 mas yr−1 accurate proper-motion cleaning and differential-reddening corrections and chose to anchor our isochrones using the lower main-sequence knee and the main-sequence turnoff prior to age determination. As a result of the data quality, we found that the K s versus F606W − K s and F336W versus F336W − K s color–magnitude diagrams were the most diagnostically powerful. We used these two color combinations to derive the stellar population ages, distances, and reddening values for both clusters. Following isochrone fitting using three different isochrone sets, we derived best-fit absolute ages of 12.2 ± 0.5 Gyr and 13.2 ± 0.4 Gyr for NGC 3201 and NGC 2298, respectively. This was done using a weighted average over the two aforementioned color combinations, following a pseudo-χ 2 determination of the best-fit isochrone set. Our derived parameters are in good agreement with recent age determinations of the two clusters, with our constraints on the ages being or ranking among the most statistically robust.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".