VLT/MAGELLAN SPECTROSCOPY OF 29 STRONG LENSING SELECTED GALAXY CLUSTERS
Bibliographic record
Abstract
ABSTRACT We present an extensive spectroscopic follow-up campaign of 29 strong lensing (SL) selected galaxy clusters discovered primarily in the Second Red-Sequence Cluster Survey (RCS-2). Our spectroscopic analysis yields redshifts for 52 gravitational arcs present in the core of our galaxy clusters, which correspond to 35 distinct background sources that are clearly distorted by the gravitational potential of these clusters. These lensed galaxies span a wide redshift range of 0.8 ≤ z ≤ 2.9, with a median redshift of z s = 1.8 ± 0.1. We also measure reliable redshifts for 1004 cluster members, allowing us to obtain robust velocity dispersion measurements for 23 of these clusters, which we then use to determine their dynamical masses by using a simulation-based σ DM − M 200 scaling relation. The redshift and mass ranges covered by our SL sample are 0.22 ≤ z ≤ 1.01 and , respectively. We analyze and quantify some possible effects that might bias our mass estimates, such as the presence of substructure, the region where cluster members are selected for spectroscopic follow-up, the final number of confirmed members, and line-of-sight effects. We find that 10 clusters of our sample with N mem ≳ 20 show signs of dynamical substructure. However, the velocity data of only one system is inconsistent with a uni-modal distribution. We therefore assume that the substructures are only marginal and not of comparable size to the clusters themselves. Consequently, our velocity dispersion and mass estimates can be used as priors for SL mass reconstruction studies and also represent an important step toward a better understanding of the properties of the SL galaxy cluster population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".