LARgE survey – III. Environments of ultra-massive passive galaxies at cosmic noon: BCG progenitors growing through mergers
Bibliographic record
Abstract
ABSTRACT We study the environments of a sample of 61 extremely rare $z\, \sim$1.6 Ultra-Massive Passively Evolving Galaxies (UMPEGs: stellar masses M⋆ >1011.5 M⊙) which – based on clustering analysis presented in Cheema et al. (2020) – appear to be associated with very massive (Mhalo ∼ 1014.1h−1 M⊙) dark matter haloes that are likely to be the progenitors of $z\, \sim$0 massive (Coma- and Virgo-like) galaxy clusters. We find that UMPEGs on average have fewer than one satellite galaxy with mass ratio $M_{\star }^{\text{sat}}$:$M_{\star }^{{\text{UMPEG}}}$ ≥ 1:5 (i.e. $M_{\star }^{\text{sat}}$$\gtrsim 10^{10.8}$ M⊙) within 0.5 Mpc; the large mass gap that we observe between the typical UMPEG and its most massive satellite implies that the $z\, \sim$1.6 UMPEGs assembled through major mergers. Using observed satellite counts with merger time-scales from the literature, we estimate the growth rate due to mergers with mass ratio of ≥1:4 to be ∼13 per cent Gyr−1 (with a $\sim \, 2 \times$ systematic uncertainty). This relatively low growth rate is unlikely to significantly affect the shape of the massive end of the stellar mass function, whose evolution must instead be driven by the quenching of new cohorts of ultra-massive star-forming galaxies. However, this growth rate is high enough that, if sustained to $z\, \sim$0, the typical $z\, \sim$1.6 $M_{\star }^{{\text{UMPEG}}}$ =1011.6 M⊙ UMPEG can grow into a M⋆ ≈1012 M⊙ brightest cluster galaxy (BCG) of a present-day massive galaxy cluster. Our observations favour a scenario in which our UMPEGs are main-branch progenitors of some of the present-day BCGs that have first assembled through major mergers at high redshifts and grown further through (likely minor) merging at later times.
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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 | 0.001 | 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".