Evidence for recent star formation in BCGs: a correspondence between blue cores and UV excess
Bibliographic record
Abstract
We present a joint analysis of near-ultraviolet (NUV) data from the GALEX (Galaxy Evolution Explorer) mission and (optical) colour profiles for a sample of seven brightest cluster galaxies (BCGs) in the Canadian Cluster Comparison Project. We find that every BCG, which has a blue rest-frame UV colour, also shows a blue core in its optical colour profile. Conversely, BCGs that lack blue cores and show monotonic colour gradients typical of old elliptical galaxies are red in the UV. We interpret this as evidence that the NUV enhancement in the blue BCGs is driven by recent star formation and not from old evolved stellar populations such as horizontal branch stars. Furthermore, the UV enhancement cannot be from an active galactic nuclei (AGN) because the spatial extent of the blue cores is significantly larger than the possible contamination region due to a massive black hole. The recent star formation in the blue BCGs typically has an age less than 200 Myr and contributes mass fractions of less than a per cent. Although the sample studied here is small, we demonstrate, for the first time, a one-to-one correspondence between blue cores in elliptical galaxies (in particular BCGs) and a NUV enhancement observed using GALEX. The combination of this one-to-one correspondence and the consistently young age of recent star formation, coupled with additional correlations with the host cluster's X-ray properties, strongly suggests that the star formation is fuelled by gas cooling out of the intracluster medium. In turn, this implies that any AGN heating of the intracluster medium in massive clusters only acts to reduce the magnitude of the cooling flow and once this flow starts, it is nearly always active. Collectively, these results suggest that AGN feedback in present-day BCGs, while important, cannot be as efficient as suggested by the recent theoretical model by proposed by De Lucia et al.
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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 | 0.002 |
| 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 | 0.001 |
| 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".