Iron and s-elements abundance variations in NGC 5286: comparison with ‘anomalous' globular clusters and Milky Way satellites
Bibliographic record
Abstract
We present a high-resolution spectroscopic analysis of 62 red giants in the Milky Way globular cluster (GC) NGC 5286.We have determined abundances of representative light protoncapture, α, Fe-peak and neutron-capture element groups, and combined them with photometry of multiple sequences observed along the colour-magnitude diagram.Our principal results are: (i) a broad, bimodal distribution in s-process element abundance ratios, with two main groups, the s-poor and s-rich groups; (ii) substantial star-to-star Fe variations, with the s-rich stars having higher Fe, e.g.[Fe/H] s-rich -[Fe/H] s-poor ∼ 0.2 dex; and (iii) the presence of O-Na-Al (anti)correlations in both stellar groups.We have defined a new photometric index, c BVI = (B -V) -(V -I), to maximize the separation in the colour-magnitude diagram between the two stellar groups with different Fe and s-element content, and this index is not significantly affected by variations in light elements (such as the O-Na anticorrelation).The variations in the overall metallicity present in NGC 5286 add this object to the class of anomalous GCs.Furthermore, the chemical abundance pattern of NGC 5286 resembles that observed in some of the anomalous GCs, e.g.M 22, NGC 1851, M 2, and the more extreme ω Centauri, that also show internal variations in s-elements, and in light elements within stars with different Fe and s-elements content.In view of the common variations in s-elements, we propose the term s-Fe-anomalous GCs to describe this sub-class of objects.The similarities in chemical abundance ratios between these objects strongly suggest similar formation and evolution histories, possibly associated with an origin in tidally disrupted dwarf satellites.
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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".