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 <z< 0.8 with masses M200 > 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".