Spectroscopic confirmation of clusters from the ESO imaging survey
Bibliographic record
Abstract
We measure redshifts for 67 galaxies in the field of six cluster candidates from the ESO Imaging Survey (EIS). The cluster candidates are selected in the EIS patches C and D among those with estimated mean redshifts 0.5 <= z <= 0.7. The observations were made with EFOSC2 at the 3.6m ESO telescope. In the six candidate cluster fields, we identify 19 possible sets of 2 to 7 galaxies in redshift space. In order to establish which of the 19 sets are likely to correspond to real dense systems we compare our counts with those expected from a uniform distribution of galaxies with given luminosity function. In order to take into account the effect of the Large Scale Structure, we modulate the probability computed from the luminosity function with random samplings of the Canada-France Redshift Survey. We find that four out of six candidate EIS clusters are likely to correspond to real systems in redshift space (> 95% confidence level). Two of these systems have mean redshift in agreement with the redshift estimate given by the matched filter algorithm (Δz = ± 0.1). The other two systems have significantly lower redshifts. We discuss the implications of our results in the context of our ongoing research projects aimed at defining high-redshift optically-selected cluster samples. Based on observations collected at the European Southern Observatory (La Silla, Chile), Proposal ID: 62.O-0601
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".