On the Assembly Bias of Cool Core Clusters Traced by Hα Nebulae
Bibliographic record
Abstract
Abstract Do cool-core (CC) and non-cool-core (NCC) clusters live in different environments? We make novel use of Hα emission lines in the central galaxies of redMaPPer clusters as proxies to construct large (1000's) samples of CC and NCC clusters and measure their relative assembly bias using both clustering and weak lensing. We increase the statistical significance of the bias measurements from clustering by cross-correlating the clusters with an external galaxy redshift catalog from the Sloan Digital Sky Survey III, the LOWZ sample. Our cross-correlations can constrain assembly bias up to a statistical uncertainty of 6%. Given our Hα criteria for CC and NCC, we find no significant differences in their clustering amplitude. Interpreting this difference as the absence of halo assembly bias, our results rule out the possibility of having different large-scale (tens of Mpc) environments as the source of diversity observed in cluster cores. Combined with recent observations of the overall mild evolution of CC and NCC properties, such as central density and CC fraction, this would suggest that either the cooling properties of the cluster core are determined early on solely by the local (<200 kpc) gas properties at formation or that local merging leads to stochastic CC relaxation and disruption in a periodic way, preserving the average population properties over time. Studying the small-scale clustering in clusters at high redshift would help shed light on the exact scenario.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".