COSMOLOGICAL CONSTRAINTS FROM GALAXY CLUSTERS IN THE 2500 SQUARE-DEGREE SPT-SZ SURVEY
Bibliographic record
Abstract
ABSTRACT We present cosmological parameter constraints obtained from galaxy clusters identified by their Sunyaev–Zel’dovich effect signature in the 2500 square-degree South Pole Telescope Sunyaev Zel’dovich (SPT-SZ) survey. We consider the 377 cluster candidates identified at with a detection significance greater than five, corresponding to the 95% purity threshold for the survey. We compute constraints on cosmological models using the measured cluster abundance as a function of mass and redshift. We include additional constraints from multi-wavelength observations, including Chandra X-ray data for 82 clusters and a weak lensing-based prior on the normalization of the mass-observable scaling relations. Assuming a spatially flat ΛCDM cosmology, we combine the cluster data with a prior on H 0 and find and , with the parameter combination . These results are in good agreement with constraints from the cosmic microwave background (CMB) from SPT, WMAP, and Planck, as well as with constraints from other cluster data sets. We also consider several extensions to ΛCDM, including models in which the equation of state of dark energy w, the species-summed neutrino mass, and/or the effective number of relativistic species ( ) are free parameters. When combined with constraints from the Planck CMB, H 0, baryon acoustic oscillation, and SNe, adding the SPT cluster data improves the w constraint by 14%, to .
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".