Radial variation of the stellar mass functions in the globular clusters M15 and M30: clues of a non-standard IMF?
Bibliographic record
Abstract
ABSTRACT We exploit a combination of high-resolution Hubble Space Telescope and wide-field ESO-VLT observations to study the slope of the global mass function (αG) and its radial variation (α(r)) in the two dense, massive and post core-collapse globular clusters M15 and M30. The available data set samples the clusters’ main sequence down to ∼0.2 M⊙ and the photometric completeness allows the study of the mass function between 0.40 M⊙ and 0.75 M⊙ from the central regions out to their tidal radii. We find that both clusters show a very similar variation in α(r) as a function of clustercentric distance. They both exhibit a very steep variation in α(r) in the central regions, which then attains almost constant values in the outskirts. Such a behaviour can be interpreted as the result of long-term dynamical evolution of the systems driven by mass-segregation and mass-loss processes. We compare these results with a set of direct N-body simulations and find that they are only able to reproduce the observed values of α(r) and αG at dynamical ages (t/trh) significantly larger than those derived from the observed properties of both clusters. We investigate possible physical mechanisms responsible for such a discrepancy and argue that both clusters might be born with a non-standard (flatter/bottom-lighter) initial mass function.
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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 | 0.000 | 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) | 0.000 | 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".