Evidence for the inside-out growth of the stellar mass distribution in galaxy clusters since
Bibliographic record
Abstract
We study the radial number density and stellar mass density distributions of satellite galaxies in a sample of 60 massive clusters at 0.04 <z< 0.26 selected from the Multi-Epoch Nearby Cluster Survey (MENeaCS) and the Canadian Cluster Comparison Project (CCCP). In addition to ~10 000 spectroscopically confirmed member galaxies, we use deep ugri-band imaging to estimate photometric redshifts and stellar masses, and then statistically subtract fore- and background sources using data from the COSMOS survey. We measure the galaxy number density and stellar mass density distributions in logarithmically spaced bins over 2 orders of magnitude in radial distance from the BCGs. For projected distances in the range 0.1 <R/R200< 2.0, we find that the stellar mass distribution is well-described by an NFW profile with a concentration of c = 2.03 ± 0.20. However, at smaller radii we measure a significant excess in the stellar mass in satellite galaxies of about 1011M⊙ per cluster, compared to these NFW profiles. We do obtain good fits to generalised NFW profiles with free inner slopes and to Einasto profiles. To examine how clusters assemble their stellar mass component over cosmic time, we compare this local sample to the GCLASS cluster sample at z ~ 1, which represents the approximate progenitor sample of the low-z clusters. This allows for a direct comparison, which suggests that the central parts (R< 0.4 Mpc) of the stellar mass distributions of satellites in local galaxy clusters are already in place at z ~ 1, and contain sufficient excess material for further BCG growth. Evolving towards z = 0, clusters appear to assemble their stellar mass primarily onto the outskirts, making them grow in an inside-out fashion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".