Major Cluster Mergers and the Location of the Brightest Cluster Galaxy
Bibliographic record
Abstract
Using a large N-body cosmological simulation combined with a subgrid treatment of galaxy formation, merging, and tidal destruction, we study the formation and evolution of the galaxy and cluster population in a comoving volume (100 Mpc)3 in a ΛCDM universe. At z = 0, our computational volume contains 1788 clusters with mass Mcl > 1.1 × 1012 M☉, including 18 massive clusters with Mcl > 1014 M☉. It also contains 1, 088, 797 galaxies with mass Mgal ⩾ 2 × 109 M☉ and luminosity L > 9.5 × 105 L☉. For each cluster, we identified the brightest cluster galaxy (BCG). We then computed two separate statistics: the fraction fBNC of clusters in which the BCG is not the closest galaxy to the center of the cluster in projection, and the ratio Δv/σ, where Δv is the difference in radial velocity between the BCG and the whole cluster and σ is the radial velocity dispersion of the cluster. We found that fBNC increases from 0.05 for low-mass clusters (Mcl ∼ 1012 M☉) to 0.5 for high-mass clusters (Mcl > 1014 M☉) with very little dependence on cluster redshift. Most of this result turns out to be a projection effect and when we consider three-dimensional distances instead of projected distances, fBNC increases only to 0.2 at high-cluster mass. The values of Δv/σ vary from 0 to 1.8, with median values in the range 0.03–0.15 when considering all clusters, and 0.12–0.31 when considering only massive clusters. These results are consistent with previous observational studies and indicate that the central galaxy paradigm, which states that the BCG should be at rest at the center of the cluster, is usually valid, but exceptions are too common to be ignored. We built merger trees for the 18 most massive clusters in the simulation. Analysis of these trees reveal that 16 of these clusters have experienced 1 or several major or semi-major mergers in the past. These mergers leave each cluster in a non-equilibrium state, but eventually the cluster settles into an equilibrium configuration, unless it is disturbed by another major or semi-major merger. We found evidence that these mergers are responsible for the off-center positions and peculiar velocities of some BCGs. Our results thus support the merging-group scenario, in which some clusters form by the merging of smaller groups in which the galaxies have already formed, including the galaxy destined to become the BCG. Finally, we argue that fBNC is not a very robust statistics, as it is very sensitive to projection and selection effects, but that Δv/σ is more robust. Still, both statistics exhibit a signature of major mergers between clusters of galaxies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".