The SUMO project I. A survey of multiple populations in globular clusters
Bibliographic record
Abstract
We present a general overview and the first results of the SUMO project (a SUrvey of Multiple pOpulations in Globular Clusters). The objective of this survey is the study of multiple stellar populations in the largest sample of globular clusters homogeneously analysed to date. To this aim we obtained high signal-to-noise (S/N > 50) photometry for main sequence stars with mass down to ∼0.5 M⊙ in a large sample of clusters using both archival and proprietary U, B, V and I data from ground-based telescopes. In this paper, we focus on the occurrence of multiple stellar populations in 23 clusters. We define a new photometric index, cU, B, I = (U − B) − (B − I), which turns out to be very effective for identifying multiple sequences along the red giant branch (RGB). We found that in the V–cU, B, I diagram all clusters presented in this paper show broadened or multimodal RGBs, with the presence of two or more components. We found a direct connection with the chemical properties of different sequences, which display different abundances of light elements (O, Na, C, N and Al). The cU, B, I index is also a powerful tool for identifying distinct sequences of stars along the horizontal branch and, for the first time in the case of NGC 104 (47 Tuc), along the asymptotic giant branch. Our results demonstrate that (i) the presence of more than two stellar populations is a common feature amongst globular clusters, as already highlighted in previous work; (ii) multiple sequences with different chemical contents can be easily identified by using standard Johnson photometry obtained with ground-based facilities; (iii) in the study of globular cluster multiple stellar populations the cU, B, I index is an alternative to spectroscopy, and has the advantage of larger statistics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".