Merger histories of brightest group galaxies from MUSE stellar kinematics
Bibliographic record
Abstract
ABSTRACT Using Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy, we analyse the stellar kinematics of 18 brightest group early-type (BGEs) galaxies, selected from the Complete Local-Volume Groups Sample (CLoGS). We analyse the kinematic maps for distinct features, and measure specific stellar angular momentum within one effective radius (λe). We classify the BGEs as fast (10/18) or slow (8/18) rotators, suggesting at least two different evolution paths. We quantify the anticorrelation between higher order kinematic moment h3 and V/σ (using the ξ3 parameter), and the kinematic misalignment angle between the photometric and kinematic position angles (using the Ψ parameter), and note clear differences between these parameter distributions of the fast and slow rotating BGEs. We find that all 10 of our fast rotators are aligned between the morphological and kinematical axis, consistent with an oblate galaxy shape, whereas the slow rotators are spread over all three classes: oblate (1/8), triaxial (4/8), and prolate (3/8). We place the results into context using known radio properties, X-ray properties, and observations of molecular gas. We find consistent merger histories inferred from observations for the fast-rotating BGEs, indicating that they experienced gas-rich mergers or interactions, and these are very likely the origin of the cold gas. Observational evidence for the slow rotators is consistent with gas-poor mergers. For the slow rotators with cold gas, all evidence point to cold gas cooling from the intragroup medium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".