DYNAMICAL ESTIMATE OF POST-MAIN-SEQUENCE STELLAR MASSES IN 47 TUCANAE
Bibliographic record
Abstract
ABSTRACT We use the effects of mass segregation on the radial distribution of different stellar populations in the core of 47 Tucanae to find estimates for the masses of stars at different post-main-sequence evolutionary stages. We take samples of main-sequence (MS) stars from the core of 47 Tucanae, at different magnitudes (i.e., different masses), and use the effects of this dynamical process to develop a relation between the radial distance (RD) at which the cumulative distribution reaches the 20th and 50th percentile and stellar mass. From these relations we estimate the masses of different post-MS populations. We find that mass remains constant for stars going through the evolutionary stages from the upper MS up to the horizontal branch (HB). By comparing RDs of the HB stars with stars of lower masses, we can exclude a mass loss greater than during the red giant branch (RGB) stage at nearly the level. The slightly higher mass estimates for the asymptotic giant branch (AGB) are consistent with the AGB having evolved from somewhat more massive stars. The AGB also exhibits evidence of contamination by more massive stars, possibly blue straggler stars (BSSs), going through the RGB phase. We do not include the BSSs in this paper due to the complexity of these objects; instead, the complete analysis of this population is left for a companion paper. The process to estimate the masses described in this paper is exclusive to the core of 47 Tuc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".