Distinct origins of environmentally quenched galaxies in the core and outer virialized regions of massive clusters at 0.8 &lt; <i>z</i> &lt; 1.5
Bibliographic record
Abstract
ABSTRACT High-redshift ($z\sim 1$) galaxy clusters are the domain where environmental quenching mechanisms are expected to emerge as important factors in the evolution of the quiescent galaxy population. Uncovering these initially subtle effects requires exploring multiple dependencies of quenching across the cluster environment, and through time. We analyse the stellar mass functions (SMFs) of 17 galaxy clusters within the GOGREEN and GCLASS surveys in the range $0.8< z<1.5$, and with $\log {(M/{\rm {M_\odot }})}>9.5$. The data are fit simultaneously with a Bayesian model that allows the Schechter function parameters of the quiescent and star-forming populations to vary smoothly with cluster-centric radius and redshift. The model also fits the radial galaxy number density profile of each population, allowing the global quenched fraction to be parametrized as a function of redshift and cluster velocity dispersion. We find the star-forming SMF to not depend on radius or redshift. For the quiescent population however, there is $\sim 2\sigma$ evidence for a radial dependence. Outside the cluster core ($R>0.3\, R_{\rm 200}$), the quenched fraction above $\log {(M/{\rm {M_\odot }})}=9.5$ is $\sim 40{\rm\,\,per\, cent}$, and the quiescent SMF is similar in shape to the star-forming field. In contrast, the cluster core has an elevated quenched fraction ($\sim 70{\rm \,\,per\, cent}$), and a quiescent SMF similar in shape to the quiescent field population. We explore contributions of ‘early mass-quenching’ and mass-independent ‘environmental-quenching’ models in each of these radial regimes. The core is well described primarily by early mass-quenching, which we interpret as accelerated quenching of massive galaxies in protoclusters, possibly through merger-driven feedback mechanisms. The non-core is better described through mass-independent environmental-quenching of the infalling field population.
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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 | 0.000 |
| 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".