Galaxy kinematics and mass calibration in massive SZE-selected galaxy clusters to<i>z</i> = 1.3
Bibliographic record
Abstract
The galaxy phase-space distribution in galaxy clusters provides insights into the formation and evolution of cluster galaxies, and it can also be used to measure cluster mass profiles. We present a dynamical study based on ∼3000 passive, non-emission-line cluster galaxies drawn from 110 galaxy clusters. The galaxy clusters were selected using the Sunyaev–Zel’dovich effect (SZE) in the 2500 deg2 South Pole Telescope (SPT)–SZ survey and cover the redshift range 0.2 < |$z$| < 1.3. We model the clusters using the Jeans equation, while adopting NFW mass profiles and a broad range of velocity dispersion anisotropy profiles. The data prefer velocity dispersion anisotropy profiles that are approximately isotropic near the centre and increasingly radial toward the cluster virial radius, and this is true for all redshifts and masses we study. The pseudo-phase-space density profile of the passive galaxies is consistent with expectations for dark matter particles and subhaloes from cosmological N-body simulations. The dynamical mass constraints are in good agreement with external mass estimates of the SPT cluster sample from either weak lensing, velocity dispersions, or X-ray YX measurements. However, the dynamical masses are lower (at the 2.2σ level) when compared to the mass calibration favoured when fitting the SPT cluster data to a Λcold dark matter model with external cosmological priors, including cosmic microwave background anisotropy data from Planck. The discrepancy grows with redshift, where in the highest redshift bin the ratio of dynamical to SPT + Planck masses is |$\eta = 0.63^{+0.13}_{-0.08}\pm 0.06$| (statistical and systematic), corresponding to a 2.6σ discrepancy.
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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 | 0.002 |
| 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 | 0.001 | 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".