Cluster magnification and the mass–richness relation in CFHTLenS
Bibliographic record
Abstract
Gravitational lensing magnification is measured with a significance of 9.7σ on a large sample of galaxy clusters in the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS). This survey covers ∼154 deg2 and contains over 18 000 cluster candidates at redshifts 0.2 ≤ z ≤ 0.9, detected using the 3D-Matched Filter cluster-finder of Milkeraitis et al. We fit composite-NFW models to the ensemble, accounting for cluster miscentring, source-lens redshift overlap, as well as nearby structure (the two-halo term), and recover mass estimates of the cluster dark matter haloes in range of ∼1013 M⊙ to 2 × 1014 M⊙. Cluster richness is measured for the entire sample, and we bin the clusters according to both richness and redshift. A mass–richness relation M200 = M0(N200/20)β is fit to the measurements. For two different cluster miscentring models, we find consistent results for the normalization and slope, M0 = (2.3 ± 0.2) × 1013 M⊙, β = 1.4 ± 0.1 and M0 = (2.2 ± 0.2) × 1013 M⊙, β = 1.5 ± 0.1. We find that accounting for the full redshift distribution of lenses and sources is important, since any overlap can have an impact on mass estimates inferred from flux magnification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".