Stellar Mass and 3.4 μm M/L Ratio Evolution of Brightest Cluster Galaxies in COSMOS since z ∼ 1.0
Bibliographic record
Abstract
Abstract We investigate the evolution of star formation rates (SFRs), stellar masses, and M/L 3.4 μm ratios of brightest cluster galaxies (BCGs) in the COSMOS survey since z ∼ 1 to determine the contribution of star formation to the growth-rate of BCG stellar mass over time. Through the spectral energy density (SED) fitting of the GALEX, CFHT, Subaru, Vista, Spitzer, and Herschel photometric data available in the COSMOS2015 catalog, we estimate the stellar mass and SFR of each BCG. We use a modified version of the iSEDfit package to fit the SEDs of our sample with both stellar and dust emission models, as well as constrain the impact of star formation history assumptions on our results. We find that in our sample of COSMOS BCGs, star formation evolves similarly to that in BCGs in samples of more massive galaxy clusters. However, compared to the latter, the magnitude of star formation in our sample is lower by ∼1 dex. Additionally, we find an evolution of BCG baryonic mass-to-light ratio (M/L 3.4 μm) with redshift which is consistent with a passively aging stellar population. We use this to build upon Wen et al.'s low-redshift νL 3.4 μm–M Stellar relation, quantifying a correlation between νL 3.4 μm and M Stellar to z ∼ 1. By comparing our results to BCGs in Sunyaev–Zel’dovich and X-ray-selected samples of galaxy clusters, we find evidence that the normalization of star formation evolution in a cluster sample is driven by the mass range of the sample and may be biased upwards by cool cores.
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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 | 0.001 | 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 | 0.000 |
| 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".