The stellar mass function and evolution of the density profile of galaxy clusters from the Hydrangea simulations at 0 &lt; <i>z</i> &lt; 1.5
Bibliographic record
Abstract
ABSTRACT Galaxy clusters are excellent probes to study the effect of environment on galaxy formation and evolution. Along with high-quality observational data, accurate cosmological simulations are required to improve our understanding of galaxy evolution in these systems. In this work, we compare state-of-the-art observational data of massive galaxy clusters ($\gt 10^{14}\, \textrm {M}_{\odot }$) at different redshifts (0 < z < 1.5) with predictions from the Hydrangea suite of cosmological hydrodynamic simulations of 24 massive galaxy clusters ($\gt 10^{14}\, \textrm {M}_{\odot }$ at z = 0). We compare three fundamental observables of galaxy clusters: the total stellar mass-to-halo mass ratio, the stellar mass function, and the radial mass density profile of the cluster galaxies. In the first two of these, the simulations agree well with the observations, albeit with a slightly too high abundance of $M_\star \lesssim 10^{10} \, \mathrm{M}_\odot$ galaxies at z ≳ 1. The Navarro–Frenk–White concentrations of cluster galaxies increase with redshift, in contrast to the decreasing dark matter (DM) halo concentrations. This previously observed behaviour is therefore due to a qualitatively different assembly of the smooth DM halo compared to the satellite population. Quantitatively, we, however, find a discrepancy in that the simulations predict higher stellar concentrations than observed at lower redshifts (z < 0.3), by a factor of ≈2. This may be due to selection bias in the simulations, or stem from shortcomings in the build-up and stripping of their inner satellite halo.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".