The rotational profiles of cluster galaxies
Bibliographic record
Abstract
ABSTRACT We compile two samples of cluster galaxies with complimentary hydrodynamic and N-body analysis using flash code to ascertain how their differing populations drive their rotational profiles and to better understand their dynamical histories. We select our main cluster sample from the X-ray Galaxy Clusters Database (BAX), which are populated with Sloan Digital Sky Survey (SDSS) galaxies. The BAX clusters are tested for the presence of substructures, acting as proxies for core mergers, culminating in sub-samples of eight merging and 25 non-merging galaxy clusters. An additional sample of 12 galaxy clusters with known dumbbell components is procured using galaxy data from the NASA/IPAC Extragalactic Database (NED) to compare against more extreme environments. BAX clusters of each sample are stacked on to a common RA–Dec. space to produce rotational profiles within the range of 0.0–2.5 r200. Merging stacks possess stronger core rotation at ≲0.5r200 primarily contributed by a red galaxy sub-population from relaxing core mergers; this is alongside high rotational velocities from blue galaxy sub-populations, until they mix and homogenize with the red sub-populations at ∼r200, indicative of an infalling blue galaxy sub-population with interactive mixing between both sub-populations at ≳ r200. flash code is utilized to simulate the merger phase between two originally independent clusters and test the evolution of their rotational profiles. Comparisons with the dumbbell clusters leads to the inference that the peculiar core rotations of some dumbbell clusters are the result of the linear motions of core galaxies relaxing on to the potential during post second infall.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".