Evidence for significant growth in the stellar mass of brightest cluster galaxies over the past 10 billion years
Bibliographic record
Abstract
Using new and published data, we construct a sample of 160 brightest cluster galaxies (BCGs) spanning the redshift interval 0.03 < z < 1.63.We use this sample, which covers 70 per cent of the history of the universe, to measure the growth in the stellar mass of BCGs after correcting for the correlation between the stellar mass of the BCG and the mass of the cluster in which it lives.We find that the stellar mass of BCGs increases by a factor of 1.8 ± 0.3 between z = 0.9 and z = 0.2.Compared to earlier works, our result is closer to the predictions of semi-analytic models.However, BCGs at z = 0.9, relative to BCGs at z = 0.2, are still a factor of 1.5 more massive than the predictions of these models.Star formation rates in BCGs at z ∼ 1 are generally too low to result in significant amounts of mass.Instead, it is likely that most of the mass build up occurs through mainly dry mergers in which perhaps half of the mass is lost to the intra-cluster medium of the cluster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".