Weak lensing mass reconstructions of the ESO Distant Cluster Survey
Bibliographic record
Abstract
We present weak lensing mass reconstructions for the 20 high-redshift clusters in the ESO Distant Cluster Survey. The weak lensing analysis was performed on deep, 3-color optical images taken with VLT/FORS2, using a composite galaxy catalog with separate shape estimators measured in each passband. We find that the EDisCS sample is composed primarily of clusters that are less massive than those in current X-ray selected samples at similar redshifts, but that all of the fields are likely to contain massive clusters rather than superpositions of low mass groups. We find that 7 of the 20 fields have additional massive structures which are not associated with the clusters and which can affect the weak lensing mass determination. We compare the mass measurements of the remaining 13 clusters with luminosity measurements from cluster galaxies selected using photometric redshifts and find evidence of a dependence of the cluster mass-to-light ratio with redshift. Finally we determine the noise level in the shear measurements for the fields as a function of exposure time and seeing and demonstrate that future ground-based surveys which plan to perform deep optical imaging for use in weak lensing measurements must achieve point-spread functions smaller than a median of $0\farcs 6$ FWHM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".