Weak-lensing-inferred scaling relations of galaxy clusters in the RCS2: mass-richness, mass-concentration, mass-bias, and more
Bibliographic record
Abstract
We study a sample of ~104 galaxy clusters in the redshift range 0.2 5×1013 h70-1 M⊙, discovered in the second Red-sequence Cluster Survey (RCS2). The depth and excellent image quality of the RCS2 enabled us to detect the cluster-mass cross-correlation up to z ~ 0.7. To obtain cluster masses, concentrations, and halo biases, we fit a cluster halo model simultaneously to the lensing signal and to the projected density profile of red-sequence cluster members, because the latter provides tight constraints on the cluster miscentring distribution. We parametrised the mass-richness relation as M200 = A × (N200/ 20)α and find A =(15.0±0.8)×1013 h70-1 M⊙ and α = 0.73 ± 0.07 at low redshift (0.2 < z < 0.35). At intermediate redshift (0.35 < z < 0.55), we find a higher normalisation, which points towards a fractional increase in the richness towards lower redshift caused by the build-up of the red sequence. The miscentring distribution is well constrained. Only ~30% of our BCGs coincide with the peak of the dark matter distribution. The distribution of the remaining BCGs are modelled with a 2D-Gaussian, whose width increases from 0.2 to 0.4 h70-1 Mpc towards higher masses. The ratio of width and r200 is constant with mass and has an average value of 0.44 ± 0.01. The mass-concentration and mass-bias relations agree fairly well with literature results at low redshift, but have a higher normalisation at higher redshifts, possibly because of selection and projection effects. The concentration of the satellite distribution decreases with mass and is correlated to the concentration of the halo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".