The evolution of brightest cluster galaxies in the nearby Universe II: The star-formation activity and the stellar mass from spectral energy distribution
Bibliographic record
Abstract
ABSTRACT We study the star-formation activity in a sample of ∼ 56 000 brightest cluster galaxies (BCGs) at 0.05 < z < 0.42 using optical and infra-red data from SDSS and WISE. We estimate stellar masses and star-formation rates (SFR) through SED fitting and study the evolution of the SFR with redshift as well as the effects of BCG stellar mass, cluster halo mass, and cooling time on star formation. Our BCGs have SFR = 1.4 × 10−3 − 275.2 [$\rm M_{\odot }$ yr−1] and sSFR = 5 × 10−15 − 6 × 10−10 [yr−1]. We find that star-forming BCGs are more abundant at higher redshifts and have higher SFR than at lower redshifts. The fraction of star-forming BCGs (fSF) varies from 30 per cent to 80 per cent at 0.05 < z < 0.42. Despite the large values of fSF, we show that only 13 per cent of the BCGs lie on the star-forming main sequence for field galaxies at the same redshifts. We also find that fSF depends only weakly on $M_{\rm 200}$, while it sharply decreases with $M_{*}$. We finally find that the SFR in BCGs decreases with increasing $t_{\rm cool}$, suggesting that star formation is related to the cooling of the intracluster medium. However, we also find a weak correlation of $M_{*}$ and $M_{\rm 200}$ with $t_{\rm cool}$ suggesting that AGNs are heating the intracluster gas around the BCGs. We compare our estimates of SFR with the predictions from empirical models for the evolution of the SFR with redshift, finding that the transition from a merger dominated to a cooling-dominated star formation may happen at z < 0.6.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".