Quantifying the universality of the stellar initial mass function in old star clusters
Bibliographic record
Abstract
We present a new technique to quantify cluster-to-cluster variations in the observed present-day stellar mass functions of a large sample of star clusters. Our method quantifies these differences as a function of both the stellar mass and the total cluster mass, and offers the advantage that it is insensitive to the precise functional form of the mass function. We applied our technique to data taken from the Advanced Camera for Surveys (ACS) Survey for Globular Clusters, from which we obtained completeness-corrected stellar mass functions in the mass range 0.25–0.75 M⊙ for a sample of 27 clusters. The results of our observational analysis were then compared to Monte Carlo simulations for globular cluster evolution spanning a range of initial mass functions, total number of stars, concentrations and virial radii. We show that the present-day mass functions of the clusters in our sample can be reproduced by assuming a universal initial mass function for all clusters, and that the cluster-to-cluster differences are consistent with what is expected from two-body relaxation. A more complete exploration of the initial cluster conditions will be needed in future studies to better constrain the precise functional form of the initial mass function. This study is a first step towards using our technique to constrain the dynamical histories of a large sample of old Galactic star clusters and, by extension, star formation in the early Universe.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".