Dynamical masses of brightest cluster galaxies – II. Constraints on the stellar IMF
Bibliographic record
Abstract
ABSTRACT We use stellar and dynamical mass profiles, combined with a stellar population analysis, of 32 brightest cluster galaxies (BCGs) at redshifts of 0.05 ≤$z$ ≤ 0.30, to place constraints on their stellar initial mass function (IMF). We measure the spatially resolved stellar population properties of the BCGs, and use it to derive their stellar mass-to-light ratios ($\Upsilon _{\star \rm POP}$). We find young stellar populations (<200 Myr) in the centres of 22 per cent of the sample, and constant $\Upsilon _{\star \rm POP}$ within 15 kpc for 60 per cent of the sample. We further use the stellar mass-to-light ratio from the dynamical mass profiles of the BCGs ($\Upsilon _{\star \rm DYN}$), modelled using a multi-Gaussian expansion and Jeans Anisotropic Method, with the dark matter contribution explicitly constrained from weak gravitational lensing measurements. We directly compare the stellar mass-to-light ratios derived from the two independent methods, $\Upsilon _{\star \rm POP}$ (assuming some IMF) to $\Upsilon _{\star \rm DYN}$ for the subsample of BCGs with no young stellar populations and constant $\Upsilon _{\star \rm POP}$. We find that for the majority of these BCGs, a Salpeter (or even more bottom-heavy) IMF is needed to reconcile the stellar population and dynamical modelling results although for a small number of BCGs, a Kroupa (or even lighter) IMF is preferred. For those BCGs better fit with a Salpeter IMF, we find that the mass-excess factor against velocity dispersion falls on an extrapolation (towards higher masses) of known literature correlations. We conclude that there is substantial scatter in the IMF amongst the highest mass galaxies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".