Locating Red Supergiants in the Galaxy NGC 6822
Bibliographic record
Abstract
Abstract Using archival near-IR photometry, we identify 51 of the K-band brightest red supergiants (RSGs) in NGC 6822 and compare their physical properties with stellar evolutionary model predictions. We first use Gaia parallax and proper motion values to filter out foreground Galactic red dwarfs before constructing a J–K versus K color–magnitude diagram to eliminate lower-mass asymptotic giant branch star contaminants in NGC 6822. We then cross match our results to previously spectroscopically confirmed RSGs and other NGC 6822 content studies and discuss our overall completeness, concluding that radial velocity alone is an insufficient method of determining membership in NGC 6822. After transforming the J and K magnitudes to effective temperatures and luminosities, we compare these physical properties with predictions from both the Geneva single-star and Binary Population and Spectral Synthesis (BPASS) single and binary-star evolution tracks. We find that our derived temperatures and luminosities match the evolutionary model predictions well, however, the BPASS model, which includes the effects of binary evolution, provides the best overall fit. This revealed the presence of a group of cool RSGs in NGC 6822, suggesting a history of binary interaction. We hope this work will lead to further comparative RSG studies in other Local Group galaxies, opportunities for direct spectroscopic follow-up, and a better understanding of evolutionary model predictions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".