The Incidence of Strong‐Lensing Clusters in the Red‐Sequence Cluster Survey
Bibliographic record
Abstract
The incidence of giant arcs due to strong-lensing clusters of galaxies is known to be discrepant with current theoretical expectations. This result derives from a comparison of several cluster samples with predictions in the framework of the currently favored ΛCDM cosmology, and one possible explanation for the discrepancy is that this cosmological model is not correct. In this paper we discuss the incidence of giant arcs in the Red-Sequence Cluster Survey (RCS), which again shows significant disagreement with theoretical predictions. We briefly describe a total of eight strong-lens systems, seven of which are discussed here for the first time. Based on the details of these systems, in particular on the ratio of single- to multiple-arc systems, we argue that it may be possible to explain this discrepancy in the currently favored cosmology by modifying the details of the lenses themselves. Specifically, the high incidence of multiple-arc systems and their overall high redshift suggests that a subpopulation of the global cluster population is responsible for much of the observed lensing. The lack of lensing clusters at z < 0.64 in the RCS indicates that a property associated with clusters at early times results in the boosted lensing cross sections; likely, a combination of ellipticity and elongation along the line of sight, substructure, and changes in the cluster-mass profiles is responsible. Cluster mass, which should evolve to globally higher values toward lower redshifts, is clearly not the most significant consideration for the formation of giant arcs.
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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) | 0.002 | 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".