The VLT-FLAMES Tarantula Survey
Bibliographic record
Abstract
Detailed studies of resolved young massive star clusters are necessary to determine their dynamical state and evaluate the importance of gas expulsion and early cluster evolution. In an effort to gain insight into the dynamical state of the young massive cluster R136 and obtain the first measurement of its velocity dispersion, we analyse multi-epoch spectroscopic data of the inner regions of 30 Doradus in the Large Magellanic Cloud obtained as part of the VLT-FLAMES Tarantula Survey. Following a quantitative assessment of the variability, we use the radial velocities of non-variable sources to place an upper limit of 6 km s-1 on the line-of-sight velocity dispersion of stars within a projected distance of 5 pc from the centre of the cluster. After accounting for the contributions of undetected binaries and measurement errors through Monte Carlo simulations, we conclude that the true velocity dispersion is likely between 4 and 5 km s-1 given a range of standard assumptions about the binary distribution. This result is consistent with what is expected if the cluster is in virial equilibrium, suggesting that gas expulsion has not altered its dynamics. We find that the velocity dispersion would be ~25 km s-1 if binaries were not identified and rejected, confirming the importance of the multi-epoch strategy and the risk of interpreting velocity dispersion measurements of unresolved extragalactic young massive clusters.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".