Galaxy Clusters in the Line of Sight to Background Quasars. I. Survey Design and Incidence of Mg<scp>ii</scp>Absorbers at Cluster Redshifts
Bibliographic record
Abstract
We describe the first optical survey of absorption systems associated with cluster galaxies at z = 0.3–0.9. We have cross-correlated quasars from the third data release of the SDSS with high-redshift cluster/group candidates from the Red-Sequence Cluster Survey. We have found 442 quasar-cluster pairs for which the Mg II λλ2796, 2803 doublet might be detected at a transverse (physical) distance d < 2 h −1 71 Mpc from the cluster centers. To investigate the incidence dN / dz and equivalent width distribution n ( W ) of Mg II systems at cluster redshifts, two statistical samples were drawn out of these pairs: one made of high-resolution spectroscopic quasar observations (46 pairs), and one made of quasars used in Mg II searches found in the literature (375 pairs). The results are (1) the population of strong Mg II systems ( W 0 2796 > 2.0 Å ) near cluster redshifts shows a significant (>3 σ) overabundance (up to a factor of 15) when compared with the "field" population; (2) the overabundance is more evident at d < 1 h 71 −1 Mpc than at d < 2 h 71 −1 Mpc , and more evident in a subsample of the most massive clusters; and (3) the population of weak Mg II systems ( W 0 2796 < 0.3 Å ) near cluster redshifts conforms to the field statistics. Unlike in the field, this dichotomy makes n ( W ) in clusters appear flat and well fitted by a power law in the entire W range. Since either the absorber number density or the filling factor/cross section affects the absorber statistics, an interesting possibility is that we have detected the signature of truncated halos due to environmental effects. Thus, the excess of strong systems is due to a population of absorbers in an overdense galaxy region, and the lack of weak systems to a different population, that got destroyed in the cluster environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".