HIGH-REDSHIFT COOL-CORE GALAXY CLUSTERS DETECTED VIA THE SUNYAEV-ZEL'DOVICH EFFECT IN THE SOUTH POLE TELESCOPE SURVEY
Bibliographic record
Abstract
We report the first investigation of cool-core properties of galaxy clusters selected via their Sunyaev–Zel'dovich (SZ) effect. We use 13 galaxy clusters uniformly selected from 178 deg 2 observed with the South Pole Telescope (SPT) and followed up by the Chandra X-ray Observatory . They form an approximately mass-limited sample (>3 × 10 14 M ☉ h −1 70 ) spanning redshifts 0.3 < z < 1.1. Using previously published X-ray-selected cluster samples, we compare two proxies of cool-core strength: surface brightness concentration ( c SB ) and cuspiness (α). We find that c SB is better constrained. We measure c SB for the SPT sample and find several new z > 0.5 cool-core clusters, including two strong cool cores. This rules out the hypothesis that there are no z > 0.5 clusters that qualify as strong cool cores at the 5.4σ level. The fraction of strong cool-core clusters in the SPT sample in this redshift regime is between 7% and 56% (95% confidence). Although the SPT selection function is significantly different from the X-ray samples, the high- z c SB distribution for the SPT sample is statistically consistent with that of X-ray-selected samples at both low and high redshifts. The cool-core strength is inversely correlated with the offset between the brightest cluster galaxy and the X-ray centroid, providing evidence that the dynamical state affects the cool-core strength of the cluster. Larger SZ-selected samples will be crucial in understanding the evolution of cluster cool cores over cosmic time.
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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 | 0.001 |
| 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".