CHEX-MATE: Exploring the kinematical properties of Planck galaxy clusters
Bibliographic record
Abstract
We analysed the kinematical properties of the CHEX-MATE galaxy cluster sample. Our study is based on the radial velocities retrieved from the SDSS DR18, DESI, and NED spectroscopic databases and new data obtained with the 10.4 m GTC and ESO-NTT telescopes. We derived cluster mass profiles for 75 clusters using the MG-MAMPOSST procedure, which recovers the gravitational potential and the anisotropy profiles from line-of-sight velocities and projected positions of galaxy members. The standard Navarro–Frenk–White (NFW) model and the Burkert model, with flatter cores than the NFW, both adequately fit the kinematic data, with only a marginal statistical preference for one model over the other. An estimation of the mass bias (1−B1) = M500SZ/M500M was performed via a comparison with Sunyaev-Zel’dovich–X-ray-calibrated mass estimates, resulting in a value of 0.54 ± 0.11 when four evidently disturbed clusters are removed from the sample. We assessed the dynamical state of the clusters by inferring the Anderson-Darling coefficient (A2) and the fraction of galaxies in substructures (fsub). Except for a few cases, we find relatively low values for A2, which suggests that CHEX-MATE clusters are not too far from relaxation. Moreover, no significant trends emerge between A2 and fsub, nor between the log-masses estimated by MG-MAMPOSST and those based on the Sunyaev–Zel’dovich effect calibrated through X-rays measurements. We studied the concentration–mass relation for the sample; despite the large scatter, we observe signs of an increasing trend for high-mass clusters, in agreement with recent theoretical expectations. Finally, our analysis of the radial anisotropy profiles of member galaxies – stacked in five bins of mass and redshift – reveals that orbits tend to be isotropic at the centre and more radial towards the edge, as found in previous studies. A slight trend of increasing radial orbits at r200 is observed in clusters with larger velocity dispersions.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".