COSMOLOGICAL CONSTRAINTS FROM SUNYAEV–ZEL'DOVICH-SELECTED CLUSTERS WITH X-RAY OBSERVATIONS IN THE FIRST 178 deg<sup>2</sup> OF THE SOUTH POLE TELESCOPE SURVEY
Bibliographic record
Abstract
We use measurements from the South Pole Telescope (SPT) Sunyaev–Zel'dovich (SZ) cluster survey in combination with X-ray measurements to constrain cosmological parameters. We present a statistical method that fits for the scaling relations of the SZ and X-ray cluster observables with mass while jointly fitting for cosmology. The method is generalizable to multiple cluster observables, and self-consistently accounts for the effects of the cluster selection and uncertainties in cluster mass calibration on the derived cosmological constraints. We apply this method to a data set consisting of an SZ-selected catalog of 18 galaxy clusters at z > 0.3 from the first 178 deg 2 of the 2500 deg 2 SPT-SZ survey, with 14 clusters having X-ray observations from either Chandra or XMM-Newton . Assuming a spatially flat ΛCDM cosmological model, we find the SPT cluster sample constrains σ 8 (Ω m /0.25) 0.30 = 0.785 ± 0.037. In combination with measurements of the cosmic microwave background (CMB) power spectrum from the SPT and the seven-year Wilkinson Microwave Anisotropy Probe data, the SPT cluster sample constrains σ 8 = 0.795 ± 0.016 and Ω m = 0.255 ± 0.016, a factor of 1.5 improvement on each parameter over the CMB data alone. We consider several extensions beyond the ΛCDM model by including the following as free parameters: the dark energy equation of state ( w ), the sum of the neutrino masses (Σ m ν ), the effective number of relativistic species ( N eff ), and a primordial non-Gaussianity ( f NL ). We find that adding the SPT cluster data significantly improves the constraints on w and Σ m ν beyond those found when using measurements of the CMB, supernovae, baryon acoustic oscillations, and the Hubble constant. Considering each extension independently, we best constrain w = −0.973 ± 0.063 and the sum of neutrino masses Σ m ν < 0.28 eV at 95% confidence, a factor of 1.25 and 1.4 improvement, respectively, over the constraints without clusters. Assuming a ΛCDM model with a free N eff and Σ m ν , we measure N eff = 3.91 ± 0.42 and constrain Σ m ν < 0.63 eV at 95% confidence. We also use the SPT cluster sample to constrain f NL = −220 ± 317, consistent with zero primordial non-Gaussianity. Finally, we discuss the current systematic limitations due to the cluster mass calibration, and future improvements for the recently completed 2500 deg 2 SPT-SZ survey. The survey has detected ∼500 clusters with a median redshift of ∼0.5 and a median mass of ∼2.3 × 10 14 M ☉ h −1 and, when combined with an improved cluster mass calibration and existing external cosmological data sets will significantly improve constraints on w .
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".