The broadening of the main sequence in the open cluster M38
Bibliographic record
Abstract
ABSTRACT Our recent multiband photometric study of the colour width of the lower main sequence of the open cluster M37 has revealed the presence of a sizeable initial chemical composition spread in the cluster. If initial chemical composition spreads are common amongst open clusters, this would have major implications for cluster formation models and the foundation of the chemical tagging technique. Here, we present a study of the unevolved main sequence of the open cluster M38, employing Gaia DR3 photometry and astrometry, together with newly acquired Sloan photometry. We have analysed the distribution of the cluster’s lower main-sequence stars with a differential colour–colour diagram made of combinations of Gaia and Sloan magnitudes, like in the study of M37. We employed synthetic stellar populations to reproduce the observed trend of M38 stars in this diagram and found that the observed colour spreads can be explained simply by the combined effect of differential reddening across the face of the cluster and the presence of unresolved binaries. There is no need to include in the synthetic sample a spread of initial chemical composition as instead necessary to explain the main sequence of M37. Further photometric investigations like ours, as well as accurate differential spectroscopic analyses on large samples of open clusters, are necessary to understand whether chemical abundance spreads are common among the open cluster population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".