SUBARU WEAK LENSING MEASUREMENTS OF FOUR STRONG LENSING CLUSTERS: ARE LENSING CLUSTERS OVERCONCENTRATED?
Bibliographic record
Abstract
We derive radial mass profiles of four strong lensing selected clusters which show prominent giant arcs (Abell 1703, SDSS J1446+3032, SDSS J1531+3414, and SDSS J2111−0115), by combining detailed strong lens modeling with weak lensing shear measured from deep Subaru Suprime-cam images. Weak lensing signals are detected at high significance for all four clusters, whose redshifts range from z = 0.28 to 0.64. We demonstrate that adding strong lensing information with known arc redshifts significantly improves constraints on the mass density profile, compared with those obtained from weak lensing alone. While the mass profiles are well fitted by the universal form predicted in N -body simulations of the Λ-dominated cold dark matter model, all four clusters appear to be slightly more centrally concentrated (the concentration parameters c vir ∼ 8) than theoretical predictions, even after accounting for the bias toward higher concentrations inherent in lensing-selected samples. Our results are consistent with previous studies which similarly detected a concentration excess, and increase the total number of clusters studied with the combined strong and weak lensing technique to 10. Combining our sample with previous work, we find that clusters with larger Einstein radii are more anomalously concentrated. We also present a detailed model of the lensing cluster Abell 1703 with constraints from multiple image families, and find the dark matter inner density profile to be cuspy with the slope consistent with −1, in agreement with expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".