Spectroscopy of clusters in the ESO Distant Cluster Survey (EDisCS)
Bibliographic record
Abstract
We present spectroscopic observations of galaxies in 4 clusters at and in one cluster at obtained with the FORS2 spectrograph on the VLT as part of the ESO Distant Cluster Survey (EDisCS), a photometric and spectroscopic survey of 20 intermediate to high redshift clusters. We describe our target selection, mask design, observation and data reduction procedures, using these first 5 clusters to demonstrate how our strategies maximise the number of cluster members for which we obtain spectroscopy. We present catalogues containing positions, I-band magnitudes and spectroscopic redshifts for galaxies in the fields of our 5 clusters. These contain 236 cluster members, with the number of members per cluster ranging from 30 to 67. Our spectroscopic success rate, i.e. the fraction of spectroscopic targets which are cluster members, averages 50% and ranges from 30% to 75%. We use a robust biweight estimator to measure cluster velocity dispersions from our spectroscopic redshift samples. We also make a first assessment of substructure within our clusters. The velocity dispersions range from 400 to 1100 . Some of the redshift distributions are significantly non-Gaussian and we find evidence for significant substructure in two clusters, one at and the other at . Both have velocity dispersions exceeding 1000 but are clearly not fully virialised; their velocity dispersions may thus be a poor indicator of their masses. The properties of these first 5 EDisCS clusters span a wide range in redshift, velocity dispersion, richness and substructure, but are representative of the sample as a whole. Spectroscopy for the full dataset will allow a comprehensive study of galaxy evolution as a function of cluster environment and redshift.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".