The <i>HST</i> Large Programme on NGC 6752 – II. Multiple populations at the bottom of the main sequence probed in NIR
Bibliographic record
Abstract
Historically, multiple populations in globular clusters (GCs) have been mostly studied from ultraviolet and optical filters down to stars that are more massive than ∼0.6 |$\mathcal {M}_{\odot }$|. Here, we exploit deep near-infrared (NIR) photometry from the Hubble Space Telescope to investigate multiple populations among M-dwarfs in the GC NGC 6752. We discovered that the three main populations (A, B, and C), previously observed in the brightest part of the colour–magnitude diagram (CMD), define three distinct sequences that run from the main-sequence (MS) knee towards the bottom of the MS (∼0.15 |${\mathcal {M}}_{\odot }$|). These results, together with similar findings on NGC 2808, M 4, and ω Centauri, demonstrate that multiple sequences of M-dwarfs are common features of the CMDs of GCs. The three sequences of low-mass stars in NGC 6752 are consistent with stellar populations with different oxygen abundances. The range of [O/Fe] needed to reproduce the NIR CMD of NGC 6752 is similar to the oxygen spread inferred from high-resolution spectroscopy of red giant branch (RGB) stars. The relative numbers of stars in the three populations of M-dwarfs are similar to those derived among RGB and MS stars more massive than ∼0.6 |$\mathcal {M}_{\odot }$|. As a consequence, the evidence that the properties of multiple populations do not depend on stellar mass is a constraint for the formation scenarios.
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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) | 0.009 | 0.002 |
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".