The young massive SMC cluster NGC 330 seen by MUSE
Bibliographic record
Abstract
Context. The origin of the initial rotation rates of stars, and how a star’s surface rotational velocity changes during the evolution, either by internal angular momentum transport or due to interactions with a binary companion, remain open questions in stellar astrophysics. Aims. Here, we aim to derive the physical parameters and study the distribution of (projected) rotational velocities of B-type stars in the ∼35 Myr-old, massive cluster NGC 330 in the Small Magellanic Cloud. NGC 330 is in an age range where the number of post-interaction binaries is predicted to be high near the cluster turnoff (TO). Methods. We developed a simultaneous photometric and spectroscopic grid-fitting method adjusting atmosphere models on multiband Hubble Space Telescope (HST) photometry and Multi Unit Spectroscopic Explorer (MUSE) spectroscopy. This allowed us to homogeneously constrain the physical parameters of over 250 B and Be stars (i.e., B-type stars with emission lines), brighter than mF814W = 18.8 mag. Results. The rotational velocities of Be stars in NGC 330 are significantly higher than the ones of B-type stars. The rotational velocities vary as a function of the star’s position in the color-magnitude diagram, qualitatively following predictions of binary population synthesis. A comparison to younger clusters shows that stars in NGC 330 rotate more rapidly on average. Conclusions. The rotational velocities of the ∼35 Myr old population in NGC 330 quantitatively agree with predictions for a stellar population that underwent significant binary interactions: the majority of the B-type stars could be single stars or primaries in pre-interaction binaries. The rapidly spinning Be stars could be mass and angular momentum gainers in previous interactions, while those Be stars close to the TO may be spun-up single stars. The slowly rotating, apparently single stars above the TO could be merger products. The different v sin i characteristics of NGC 330 compared to younger populations can be understood in this framework.
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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 | 0.000 | 0.000 |
| 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".