CFHTLenS: a weak lensing shear analysis of the 3D-Matched-Filter galaxy clusters
Bibliographic record
Abstract
We present the cluster mass-richness scaling relation calibrated by a weak lensing analysis of ≳ 18 000 galaxy cluster candidates in the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS). Detected using the 3D-Matched-Filter (MF) cluster-finder of Milkeraitis et al., these cluster candidates span a wide range of masses, from the small group scale up to ∼1015 M⊙, and redshifts 0.2 ≲ z ≲ 0.9. The total significance of the stacked shear measurement amounts to 54σ. We compare cluster masses determined using weak lensing shear and magnification, finding the measurements in individual richness bins to yield 1σ compatibility, but with magnification estimates biased low. This first direct mass comparison yields important insights for improving the systematics handling of future lensing magnification work. In addition, we confirm analyses that suggest cluster miscentring has an important effect on the observed 3D-MF halo profiles, and we quantify this by fitting for projected cluster centroid offsets, which are typically ∼0.4 arcmin. We bin the cluster candidates as a function of redshift, finding similar cluster masses and richness across the full range up to z ∼ 0.9. We measure the 3D-MF mass-richness scaling relation M200 = M0(N200/20)β. We find a normalization |$M_0 \sim (2.7^{+0.5}_{-0.4}) \times 10^{13} \,\mathrm{M}_{{\odot }}$|, and a logarithmic slope of β ∼ 1.4 ± 0.1, both of which are in 1σ agreement with results from the magnification analysis. We find no evidence for a redshift dependence of the normalization. The CFHTLenS 3D-MF cluster catalogue is now available at cfhtlens.org.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".