THE INSIDE-OUT GROWTH OF THE MOST MASSIVE GALAXIES AT 0.3 <<i>z</i>< 0.9
Bibliographic record
Abstract
We study the surface brightness profiles of a sample of brightest cluster galaxies (BCGs) with 0.3 < z < 0.9. The BCGs are selected from the first Red-sequence Cluster Survey and an X-ray cluster survey. The surface brightness profiles of the BCGs are measured using HST ACS images, and the majority of them can be well modeled by a single Sérsic profile with a typical Sérsic index n ∼ 6 and a half-light radius ∼30 kpc. Although the single Sérsic model fits the profiles well, we argue that the systematics in the sky background measurement and the coupling between the model parameters make the comparison of the best-fit model parameters ambiguous. Direct comparison of the BCG profiles, on the other hand, has revealed an inside-out growth for these most massive galaxies: as the mass of a BCG increases, the central mass density of the galaxy increases slowly ( ), while the slope of the outer profile grows continuously shallower ( ). Such a fashion of growth continues down to the less massive early-type galaxies (ETGs) as a smooth function of galaxy mass, without apparent distinction between BCGs and non-BCGs. For the very massive ETGs and BCGs, the slope of the Kormendy relation starts to trace the slope of the surface brightness profiles and becomes insensitive to subtle profile evolution. These results are generally consistent with dry mergers being the major driver of the mass growth for BCGs and massive ETGs. We also find strong correlations between the richness of clusters and the properties of BCGs: the more massive the clusters are, the more massive the BCGs ( ) and the shallower their surface brightness profiles. After taking into account the bias in the cluster samples, we find the masses of the BCGs have grown by at least a factor of 1.5 from z = 0.5 to z = 0, in contrast to the previous findings of no evolution. Such an evolution validates the expectation from the ΛCDM model.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".