A SUNYAEV-ZEL'DOVICH-SELECTED SAMPLE OF THE MOST MASSIVE GALAXY CLUSTERS IN THE 2500 deg<sup>2</sup>SOUTH POLE TELESCOPE SURVEY
Bibliographic record
Abstract
The South Pole Telescope (SPT) is currently surveying 2500 deg 2 of the southern sky to detect massive galaxy clusters out to the epoch of their formation using the Sunyaev–Zel'dovich (SZ) effect. This paper presents a catalog of the 26 most significant SZ cluster detections in the full survey region. The catalog includes 14 clusters which have been previously identified and 12 that are new discoveries. These clusters were identified in fields observed to two differing noise depths: 1500 deg 2 at the final SPT survey depth of 18 μK arcmin at 150 GHz and 1000 deg 2 at a depth of 54 μK arcmin. Clusters were selected on the basis of their SZ signal-to-noise ratio (S/N) in SPT maps, a quantity which has been demonstrated to correlate tightly with cluster mass. The S/N thresholds were chosen to achieve a comparable mass selection across survey fields of both depths. Cluster redshifts were obtained with optical and infrared imaging and spectroscopy from a variety of ground- and space-based facilities. The redshifts range from 0.098 ⩽ z ⩽ 1.132 with a median of z med = 0.40. The measured SZ S/N and redshifts lead to unbiased mass estimates ranging from 9.8 × 10 14 M ☉ h −1 70 ⩽ M 200 (ρ mean ) ⩽ 3.1 × 10 15 M ☉ h −1 70 . Based on the SZ mass estimates, we find that none of the clusters are individually in significant tension with the ΛCDM cosmological model. We also test for evidence of non-Gaussianity based on the cluster sample and find the data show no preference for non-Gaussian perturbations.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".