EDisCS -- the ESO Distant Cluster Survey -- Sample Definition and Optical Photometry
Bibliographic record
Abstract
We present the ESO Distant Cluster Survey (EDisCS) a survey of 20 fields containing distant galaxy clusters with redshifts ranging from 0.4 to almost 1.0. Candidate clusters were chosen from among the brightest objects identified in the Las Campanas Distant Cluster Survey, half with estimated redshift z_est~0.5 and half with z_est~0.8. They were confirmed by identifying red sequences in moderately deep two colour data from VLT/FORS2. For confirmed candidates we have assembled deep three-band optical photometry using VLT/FORS2, deep near-infrared photometry in one or two bands using NTT/SOFI, deep optical spectroscopy using VLT/FORS2, wide field imaging in two or three bands using the ESO Wide Field Imager, and HST/ACS mosaic images for 10 of the most distant clusters. This first paper presents our sample and the VLT photometry we have obtained. We present images, colour-magnitude diagrams and richness estimates for our clusters, as well as giving redshifts and positions for the brightest cluster members. Subsequent papers will present our infrared photometry, spectroscopy, HST and wide-field imaging, as well as a wealth of further analysis and science results. Our reduced data become publicly available as these papers are accepted.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".