Multimass modelling of Milky Way globular clusters – I. Implications on their stellar initial mass function above 1 M⊙
Bibliographic record
Abstract
ABSTRACT The distribution of stars and stellar remnants (white dwarfs, neutron stars, and black holes) within globular clusters holds clues about their formation and long-term evolution, with important implications for their initial mass function (IMF) and the formation of black hole mergers. In this work, we present best-fitting multimass models for 37 Milky Way globular clusters, which were inferred from various data sets, including proper motions from Gaia EDR3 and HST, line-of-sight velocities from ground-based spectroscopy and deep stellar mass functions from HST. We use metallicity-dependent stellar evolution recipes to obtain present-day mass functions of stars and remnants from the IMF. By dynamically probing the present-day mass function of all objects in a cluster, including the mass distribution of remnants, these models allow us to explore in detail the stellar (initial) mass functions of a large sample of Milky Way GCs. We show that, while the low-mass mass function slopes are strongly dependent on the dynamical age of the clusters, the high-mass slope (α3; m > 1 M⊙) is not, indicating that the mass function in this regime has generally been less affected by dynamical mass loss. Examination of this high-mass mass function slope suggests an IMF in this mass regime consistent with a Salpeter IMF is required to reproduce the observations. This high-mass IMF is incompatible with a top-heavy IMF, as has been proposed recently. Finally, based on multimass model fits to our sample of Milky Way GCs, no significant correlation is found between the high-mass IMF slope and cluster metallicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".