GEMINI/GMOS SPECTROSCOPY OF 26 STRONG-LENSING-SELECTED GALAXY CLUSTER CORES
Bibliographic record
Abstract
We present results from a spectroscopic program targeting 26 strong-lensing cluster cores that were visually identified in the Sloan Digital Sky Survey (SDSS) and the Second Red-Sequence Cluster Survey (RCS-2). The 26 galaxy cluster lenses span a redshift range of 0.2 < z < 0.65, and our spectroscopy reveals 69 unique background sources with redshifts as high as z = 5.200. We also identify redshifts for 262 cluster member galaxies and measure the velocity dispersions and dynamical masses for 18 clusters where we have redshifts for N ⩾ 10 cluster member galaxies. We account for the expected biases in dynamical masses of strong-lensing-selected clusters as predicted by results from numerical simulations and discuss possible sources of bias in our observations. The median dynamical mass of the 18 clusters with N ⩾ 10 spectroscopic cluster members is M Vir = 7.84 × 10 14 M ☉ h −1 0.7 , which is somewhat higher than predictions for strong-lensing-selected clusters in simulations. The disagreement is not significant considering the large uncertainty in our dynamical data, systematic uncertainties in the velocity dispersion calibration, and limitations of the theoretical modeling. Nevertheless our study represents an important first step toward characterizing large samples of clusters that are identified in a systematic way as systems exhibiting dramatic strong-lensing features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".