MASS–CONCENTRATION RELATION OF CLUSTERS OF GALAXIES FROM CFHTLenS
Bibliographic record
Abstract
Based on weak lensing data from the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS), in this paper we study the mass–concentration ( M – c ) relation for ∼200 redMaPPer clusters in the fields. We extract the M – c relation by measuring the density profiles of individual clusters instead of using stacked weak lensing signals. By performing Monte Carlo simulations, we demonstrate that although the signal-to-noise ratio for each individual cluster is low, the unbiased M – c relation can still be reliably derived from a large sample of clusters by carefully taking into account the impacts of shape noise, cluster center offset, dilution effect from member or foreground galaxies, and the projection effect. Our results show that within error bars the derived M – c relation for redMaPPer clusters is in agreement with simulation predictions. There is a weak deviation in that the halo concentrations calibrated by Monte Carlo simulations are somewhat higher than that predicted from Planck cosmology.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".