The assembly of the Virgo cluster, traced by its galaxy haloes
Bibliographic record
Abstract
ABSTRACT Kinematic studies have produced accurate measurements of the total dark matter mass and mean dark matter density within the optical extent of galaxies for large samples of objects. Here we consider theoretical predictions for the latter quantity, $\bar{\rho }_{\rm dm}$, measured within the isophotal radius R23.5, for isolated haloes with universal density profiles. Through a combination of empirical scaling relations, we show that $\bar{\rho }_{\rm dm}$ is expected to depend weakly on halo mass and strongly on redshift. When galaxy haloes fall into larger groups or clusters, they become tidally stripped, reducing their total dark matter mass, but this process is expected to preserve central density until an object is close to disruption. We confirm this with collisonless simulations of cluster formation, finding that subhaloes have values of $\bar{\rho }_{\rm dm}$ close to the analytic predictions. This suggests that $\bar{\rho }_{\rm dm}$ may be a useful indicator of infall redshift on to the cluster. We test this hypothesis with data from the SHIVir survey, which covers a reasonable fraction of the Virgo cluster. We find that galaxies with high $\bar{\rho }_{\rm dm}$ do indeed trace the densest regions of the cluster, with a few notable exceptions. Samples selected by environment have higher densities at a significance of 3.5–4σ, while samples selected by density are more clustered at 3–3.5σ significance. We conclude that halo density can be a powerful tracer of the assembly history of clusters and their member 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 | 0.001 |
| 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 | 0.000 |
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".