On the dwarf irregular galaxy NGC 6822. I. Young, intermediate, and old stellar populations
Bibliographic record
Abstract
We present accurate and deep multi-band ($g,r,i$) photometry of the Local Group dwarf irregular galaxy NGC 6822. The images were collected with wide field cameras at 2m/4m- (INT,CTIO,CFHT) and 8m-class telescopes (SUBARU) covering a 2 square degrees FoV across the center of the galaxy. We performed PSF photometry of $\approx$7,000 CCD images and the final catalog includes more than 1 million objects. We developed a new approach to identify candidate field and galaxy stars, and performed a new estimate of the galaxy center by using old stellar tracers finding that it differs by 1.15 (RA) and 1.53 (DEC) arcmin from previous estimates. We also found that young (Main Sequence, Red Supergiants), intermediate (Red Clump, Asymptotic Giant Branch [AGB]) and old (Red Giant Branch [RGB]) stars display different radial distributions. Old stellar population is spherically distributed and extends to radial distances larger than previously estimated ($\sim$1 degree). The young population shows a well defined bar and a disk-like distribution, as suggested by radio measurements, that is off-center compared with old population. We discuss pros and cons of the different diagnostics adopted to identify AGB stars and develop new ones based on optical-NIR-MIR color-color diagrams (CCDs) to characterize Oxygen and Carbon (C) rich stars. We found a mean population ratio between Carbon and M-type (C/M) stars of 0.67$\pm$0.08 (optical/NIR/MIR) and we used the observed C/M ratio with empirical C/M-metallicity relations to estimate a mean iron abundance of [Fe/H]$\sim$-1.25 ($\sigma$=0.04 dex) that agrees quite well with literature estimates.
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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 | 0.000 |
| 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".